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  • Public Library: [methods_func]
  • analyse
    Input: analyse("function","variable") Example: analyse(sin(x),x) Summary: - Maximal domain - Axial intercepts - Derivative - Stationary points and their nature - Inflection points and their nature Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • function_inv
    Input: function_inv("function of x") Example: function_inv(x^2) Summary: Finds the inverse function. Notes: Function must be in terms of "x"
  • intersect
    Input:​​ intesect("function 1","function 2","variable") Example: intersect(x/2,3log(x),x) Summary: Finds the intersection points (x&y coordinates) of 2 functions. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • transform
    Input: transform("function","variable") Example: transform(sin(x),x) Summary: Functionality #1: Applying transformations to functions: - Finds the image of a function under given transformations - Finds the transformation matrix Functionality #2: - Finds the transformations between two functions given a base function - Finds the transformation matrix - Lists the transformations Functionality #3: - Finds the pre-image of a function under known transformations - Finds the transformation matrix Functionality #4: - Finds the image of a point under given transformations
  • Public Library: [methods_diffcalc]
  • avg_change
    Input:​​ avg_change("function","variable","lower bound","upper bound") Example: avg_change(log(x),x,3,7) Summary: Finds the average rate of change of a function between two endpoints. Notes: Uses the formula (f(b)-f(a))/(b-a) to calculate the average change.
  • function_grad
    Input:​ ​ function_grad("function","variable","point") Example: function_grad(x^2,x,3) - {finds gradient of x^2 at x=3) Summary: Finds the gradient of a function at a point.
  • function_lines
    Input:​​ function_lines("function","variable","point") Example: function_lines(sin(x),x,pi) Summary: - Finds the derivative. - Finds equation of the tangent line at a point. - Finds axial intercepts of the tangent line. - Finds equation of the normal line at a point. - Finds the axial intercepts of the normal line.
  • normal_line
    Input:​​ normal_line("function","variable","point") Example: normal_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient and perpendicular gradient. - Finds the axial intercepts of the normal line. - Finds the equation of the normal line.
  • solve_grad
    Input:​​ solve_grad("function","variable","gradient") Example: solve_grad(tan(x),x,1) Summary: - Finds the derivative - Finds the points (x&y coordinates) on the function with the specified gradient. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • analyse
    Enter your answer here
  • tangent_line
    Input:​​ tangent_line("function","variable","point") Example: tangent_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient. - Finds the axial intercepts of the tangent line. - Finds the equation of the tangent line.
  • function_inv
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  • intersect
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  • transform
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  • area_2curves
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  • function_avg
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  • riemann_sum
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  • total_area
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  • discriminant
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  • linear_info
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  • normal_line_w
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  • tangent_line_w
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  • system_solve (in development)
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  • binom_cdf
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  • binom_info
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  • binom_inv
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  • binom_pdf
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  • normal
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  • normal_inv
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  • z_score
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  • confidence
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  • margin
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  • p_binom_info
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  • p_small_info
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  • discrete_info
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  • continuous_info
    Enter your answer here
  • Public Library: [methods_intcalc]
  • area_2curves
    Input: area_2curves("function 1","function 2","variable","lower bound","upper bound") Example: area_2curves(sin(x),cos(x),x,0,5) Summary: - Finds the total bounded area between 2 curves. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • function_avg
    Input: function_avg("function","variable","lower bound","upper bound") Example: function_avg(log(x),x,1,2) Summary: Finds the average value of a function over an interval.
  • riemann_sum
    Input: riemann_sum("function","variable","lower bound","upper bound") Example: riemann_sum(sin(x),x,0,pi/2) Summary: - Finds the approximated area under a curve using the left endpoint rule. or: - Finds the approximated area under a curve using the right endpoint rule. or: - Finds the approximated area under a curve using the midpoint rule. and: - Finds the actual area under the curve. Notes: *Enter number of partitions desired in the chosen interval *Enter "1", "2" or "3" to choose type of approximation.
  • total_area
    Input: total_area("function","variable","lower bound","upper bound") Example: total_area(cos(2x),x,0,5) Summary: - Finds the total bounded area between a function and the horizontal axis. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • Public Library: [methods_prblty]
  • binom_cdf
    Input: binom_cdf() Summary: Finds the binomial CDF probability. (Probability over a range of values) Notes: Enter parameters as prompted.
  • binom_info
    Input: binom_info() Summary: - Finds mean, variance and standard deviation of a binomial probability distribution. - Calculates all possible probability values of the distribution. Notes: Enter parameters as prompted. *To generate a probability table, enter "1" when prompted. *Entering a 0 will not generate a probability table.
  • binom_inv
    Input: binom_inv() Summary: Finds the minimum number of trials required to meet a certain probability. Notes:
  • binom_pdf
    Input: binom_pdf() Summary: Finds the binomial PDF probability. (Probability at a given value) Notes: Enter parameters as prompted.
  • normal
    Input: normal() Summary: Finds the probabilities of a normal distribution. Notes: Enter parameters as prompted.
  • normal_inv
    Input: normal_inv() Summary: Finds the value for which a specific probability occurs. Notes: Enter parameters as prompted.
  • normal_solve (unreleased)
    Input: normal_solve() Summary: - Finds the mean given specified parameters and probabilities. or: - Finds the standard deviation given specified parameters and probabilities. or: - Finds both the mean and standard deviation given known probabilities. Notes: Enter parameters as prompted.
  • z_score
    Input: z_score() Summary: Standardises probability values on a normal distrubution. Notes: Enter parameters as prompted.
  • discrete_info
    Input: discrete_info({"sample space"},{"associated probabilities"} Example: discrete_info({1,2,3},{0.3,0.6,0.1}) *where: Pr(1)=0.3 , Pr(2)=0.6 , Pr(3) = 0.1 Summary: - Generates the probability distribution table - Finds the mean, variance and standard deviation of the probability distribution. Notes: The program will automatically check for the conditions for which: - The sum of all probabilities is 1 - Both lists have the same dimension Otherwise, the program will terminate.
  • continuous_info
    Input: continuous_info("probability density function",{"lower bound","upper bound"} Example: continuous_info(x,{0,sqrt(2)}) Summary: - Finds the mean, variance and standard deviation of the probability distribution. - Finds the 1st and 3rd quartiles, interquartile range, and median. Notes: *Function must be in terms of "x" *Hybrid functions can be used (lower and upper bounds still need to be specified) The program will automatically check for the conditions for which: - The function in its given domain is a probability density function Otherwise, the program will terminate.
  • Public Library: [methods_stats]
    not in this study design :(
  • confidence
    Enter your answer here
  • margin
    Enter your answer here
  • p_binom_info
    Enter your answer here
  • p_small_info
    Enter your answer here
  • Public Library: [methods_misc]
  • discriminant
    Input: discriminant("quadratic","variable") Example: discriminant(x^2-x+2,x) Summary: - Calculates the discriminant of a quadratic. or: - Finds when the quadratic has two, one or no solutions. Example 2: discriminant(x^2+c,x) *Choose [2] to "solve discriminant". *Enter the unknown variable (in this case, "c") *Choose [1],[2] or [3] to select disriminant condition.
  • linear_info
    Input: linear_info([x1,y1],[x2,y2]) or: linear_info([x,y],"gradient") Example: linear_info([1,2],[5,6]) or: linear_info([1,2],4) Summary: - Finds the midpoint, distance and gradient between 2 points. - Finds the equation of the linear line between 2 points and its axial intercepts. or: - Finds the equation of a linear line and its axial intercepts, given a point and a gradient. Notes: *Square brackets must be used to represent coordinate pairs.
  • normal_line_w
    Input: normal_line_w("function","variable","point") Example: normal_line_w(sin(x),x,3) Summary: Shows all working out to obtain the normal line.
  • tangent_line_w
    Input: tangent_line_w("function","variable","point") Example: tangent_line_w(sin(x),x,3) Summary: Shows all working out to obtain the tangent line.
  • system_solve (unreleased)
    Input: sytem_solve("coefficient matrix","output vector","variable") Example: Summary: Calculates when a linear system of 2 variables has a unique solution, no solution or infinitely many solutions. How to enter a matrix: - Method 1: Use the template menu on the left of the catalogue button. - Method 2: Press [menu][7][1][1] - Method 3: Press [shift][return] to add column or [return] to add a row inside of a square bracket.
  • Public Library: [methods_func]
  • analyse
    Input: analyse("function","variable") Example: analyse(sin(x),x) Summary: - Maximal domain - Axial intercepts - Derivative - Stationary points and their nature - Inflection points and their nature Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • function_inv
    Input: function_inv("function of x") Example: function_inv(x^2) Summary: Finds the inverse function. Notes: Function must be in terms of "x"
  • intersect
    Input:​​ intesect("function 1","function 2","variable") Example: intersect(x/2,3log(x),x) Summary: Finds the intersection points (x&y coordinates) of 2 functions. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • transform
    Input: transform("function","variable") Example: transform(sin(x),x) Summary: Functionality #1: Applying transformations to functions: - Finds the image of a function under given transformations - Finds the transformation matrix Functionality #2: - Finds the transformations between two functions given a base function - Finds the transformation matrix - Lists the transformations Functionality #3: - Finds the pre-image of a function under known transformations - Finds the transformation matrix Functionality #4: - Finds the image of a point under given transformations
  • Public Library: [methods_diffcalc]
  • avg_change
    Input:​​ avg_change("function","variable","lower bound","upper bound") Example: avg_change(log(x),x,3,7) Summary: Finds the average rate of change of a function between two endpoints. Notes: Uses the formula (f(b)-f(a))/(b-a) to calculate the average change.
  • function_grad
    Input:​ ​ function_grad("function","variable","point") Example: function_grad(x^2,x,3) - {finds gradient of x^2 at x=3) Summary: Finds the gradient of a function at a point.
  • function_lines
    Input:​​ function_lines("function","variable","point") Example: function_lines(sin(x),x,pi) Summary: - Finds the derivative. - Finds equation of the tangent line at a point. - Finds axial intercepts of the tangent line. - Finds equation of the normal line at a point. - Finds the axial intercepts of the normal line.
  • normal_line
    Input:​​ normal_line("function","variable","point") Example: normal_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient and perpendicular gradient. - Finds the axial intercepts of the normal line. - Finds the equation of the normal line.
  • solve_grad
    Input:​​ solve_grad("function","variable","gradient") Example: solve_grad(tan(x),x,1) Summary: - Finds the derivative - Finds the points (x&y coordinates) on the function with the specified gradient. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • analyse
    Enter your answer here
  • tangent_line
    Input:​​ tangent_line("function","variable","point") Example: tangent_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient. - Finds the axial intercepts of the tangent line. - Finds the equation of the tangent line.
  • function_inv
    Enter your answer here
  • intersect
    Enter your answer here
  • transform
    Enter your answer here
  • area_2curves
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  • function_avg
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  • riemann_sum
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  • total_area
    Enter your answer here
  • discriminant
    Enter your answer here
  • linear_info
    Enter your answer here
  • normal_line_w
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  • tangent_line_w
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  • system_solve (in development)
    Enter your answer here
  • binom_cdf
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  • binom_info
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  • binom_inv
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  • binom_pdf
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  • normal
    Enter your answer here
  • normal_inv
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  • z_score
    Enter your answer here
  • confidence
    Enter your answer here
  • margin
    Enter your answer here
  • p_binom_info
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  • p_small_info
    Enter your answer here
  • discrete_info
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  • continuous_info
    Enter your answer here
  • Public Library: [methods_intcalc]
  • area_2curves
    Input: area_2curves("function 1","function 2","variable","lower bound","upper bound") Example: area_2curves(sin(x),cos(x),x,0,5) Summary: - Finds the total bounded area between 2 curves. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • function_avg
    Input: function_avg("function","variable","lower bound","upper bound") Example: function_avg(log(x),x,1,2) Summary: Finds the average value of a function over an interval.
  • riemann_sum
    Input: riemann_sum("function","variable","lower bound","upper bound") Example: riemann_sum(sin(x),x,0,pi/2) Summary: - Finds the approximated area under a curve using the left endpoint rule. or: - Finds the approximated area under a curve using the right endpoint rule. or: - Finds the approximated area under a curve using the midpoint rule. and: - Finds the actual area under the curve. Notes: *Enter number of partitions desired in the chosen interval *Enter "1", "2" or "3" to choose type of approximation.
  • total_area
    Input: total_area("function","variable","lower bound","upper bound") Example: total_area(cos(2x),x,0,5) Summary: - Finds the total bounded area between a function and the horizontal axis. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • Public Library: [methods_prblty]
  • binom_cdf
    Input: binom_cdf() Summary: Finds the binomial CDF probability. (Probability over a range of values) Notes: Enter parameters as prompted.
  • binom_info
    Input: binom_info() Summary: - Finds mean, variance and standard deviation of a binomial probability distribution. - Calculates all possible probability values of the distribution. Notes: Enter parameters as prompted. *To generate a probability table, enter "1" when prompted. *Entering a 0 will not generate a probability table.
  • binom_inv
    Input: binom_inv() Summary: Finds the minimum number of trials required to meet a certain probability. Notes:
  • binom_pdf
    Input: binom_pdf() Summary: Finds the binomial PDF probability. (Probability at a given value) Notes: Enter parameters as prompted.
  • normal
    Input: normal() Summary: Finds the probabilities of a normal distribution. Notes: Enter parameters as prompted.
  • normal_inv
    Input: normal_inv() Summary: Finds the value for which a specific probability occurs. Notes: Enter parameters as prompted.
  • normal_solve (unreleased)
    Input: normal_solve() Summary: - Finds the mean given specified parameters and probabilities. or: - Finds the standard deviation given specified parameters and probabilities. or: - Finds both the mean and standard deviation given known probabilities. Notes: Enter parameters as prompted.
  • z_score
    Input: z_score() Summary: Standardises probability values on a normal distrubution. Notes: Enter parameters as prompted.
  • discrete_info
    Input: discrete_info({"sample space"},{"associated probabilities"} Example: discrete_info({1,2,3},{0.3,0.6,0.1}) *where: Pr(1)=0.3 , Pr(2)=0.6 , Pr(3) = 0.1 Summary: - Generates the probability distribution table - Finds the mean, variance and standard deviation of the probability distribution. Notes: The program will automatically check for the conditions for which: - The sum of all probabilities is 1 - Both lists have the same dimension Otherwise, the program will terminate.
  • continuous_info
    Input: continuous_info("probability density function",{"lower bound","upper bound"} Example: continuous_info(x,{0,sqrt(2)}) Summary: - Finds the mean, variance and standard deviation of the probability distribution. - Finds the 1st and 3rd quartiles, interquartile range, and median. Notes: *Function must be in terms of "x" *Hybrid functions can be used (lower and upper bounds still need to be specified) The program will automatically check for the conditions for which: - The function in its given domain is a probability density function Otherwise, the program will terminate.
  • Public Library: [methods_stats]
    not in this study design :(
  • confidence
    Enter your answer here
  • margin
    Enter your answer here
  • p_binom_info
    Enter your answer here
  • p_small_info
    Enter your answer here
  • Public Library: [methods_misc]
  • discriminant
    Input: discriminant("quadratic","variable") Example: discriminant(x^2-x+2,x) Summary: - Calculates the discriminant of a quadratic. or: - Finds when the quadratic has two, one or no solutions. Example 2: discriminant(x^2+c,x) *Choose [2] to "solve discriminant". *Enter the unknown variable (in this case, "c") *Choose [1],[2] or [3] to select disriminant condition.
  • linear_info
    Input: linear_info([x1,y1],[x2,y2]) or: linear_info([x,y],"gradient") Example: linear_info([1,2],[5,6]) or: linear_info([1,2],4) Summary: - Finds the midpoint, distance and gradient between 2 points. - Finds the equation of the linear line between 2 points and its axial intercepts. or: - Finds the equation of a linear line and its axial intercepts, given a point and a gradient. Notes: *Square brackets must be used to represent coordinate pairs.
  • normal_line_w
    Input: normal_line_w("function","variable","point") Example: normal_line_w(sin(x),x,3) Summary: Shows all working out to obtain the normal line.
  • tangent_line_w
    Input: tangent_line_w("function","variable","point") Example: tangent_line_w(sin(x),x,3) Summary: Shows all working out to obtain the tangent line.
  • system_solve (unreleased)
    Input: sytem_solve("coefficient matrix","output vector","variable") Example: Summary: Calculates when a linear system of 2 variables has a unique solution, no solution or infinitely many solutions. How to enter a matrix: - Method 1: Use the template menu on the left of the catalogue button. - Method 2: Press [menu][7][1][1] - Method 3: Press [shift][return] to add column or [return] to add a row inside of a square bracket.
  • Public Library: [methods_func]
  • analyse
    Input: analyse("function","variable") Example: analyse(sin(x),x) Summary: - Maximal domain - Axial intercepts - Derivative - Stationary points and their nature - Inflection points and their nature Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • function_inv
    Input: function_inv("function of x") Example: function_inv(x^2) Summary: Finds the inverse function. Notes: Function must be in terms of "x"
  • intersect
    Input:​​ intesect("function 1","function 2","variable") Example: intersect(x/2,3log(x),x) Summary: Finds the intersection points (x&y coordinates) of 2 functions. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • transform
    Input: transform("function","variable") Example: transform(sin(x),x) Summary: Functionality #1: Applying transformations to functions: - Finds the image of a function under given transformations - Finds the transformation matrix Functionality #2: - Finds the transformations between two functions given a base function - Finds the transformation matrix - Lists the transformations Functionality #3: - Finds the pre-image of a function under known transformations - Finds the transformation matrix Functionality #4: - Finds the image of a point under given transformations
  • Public Library: [methods_diffcalc]
  • avg_change
    Input:​​ avg_change("function","variable","lower bound","upper bound") Example: avg_change(log(x),x,3,7) Summary: Finds the average rate of change of a function between two endpoints. Notes: Uses the formula (f(b)-f(a))/(b-a) to calculate the average change.
  • function_grad
    Input:​ ​ function_grad("function","variable","point") Example: function_grad(x^2,x,3) - {finds gradient of x^2 at x=3) Summary: Finds the gradient of a function at a point.
  • function_lines
    Input:​​ function_lines("function","variable","point") Example: function_lines(sin(x),x,pi) Summary: - Finds the derivative. - Finds equation of the tangent line at a point. - Finds axial intercepts of the tangent line. - Finds equation of the normal line at a point. - Finds the axial intercepts of the normal line.
  • normal_line
    Input:​​ normal_line("function","variable","point") Example: normal_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient and perpendicular gradient. - Finds the axial intercepts of the normal line. - Finds the equation of the normal line.
  • solve_grad
    Input:​​ solve_grad("function","variable","gradient") Example: solve_grad(tan(x),x,1) Summary: - Finds the derivative - Finds the points (x&y coordinates) on the function with the specified gradient. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • analyse
    Enter your answer here
  • tangent_line
    Input:​​ tangent_line("function","variable","point") Example: tangent_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient. - Finds the axial intercepts of the tangent line. - Finds the equation of the tangent line.
  • function_inv
    Enter your answer here
  • intersect
    Enter your answer here
  • transform
    Enter your answer here
  • area_2curves
    Enter your answer here
  • function_avg
    Enter your answer here
  • riemann_sum
    Enter your answer here
  • total_area
    Enter your answer here
  • discriminant
    Enter your answer here
  • linear_info
    Enter your answer here
  • normal_line_w
    Enter your answer here
  • tangent_line_w
    Enter your answer here
  • system_solve (in development)
    Enter your answer here
  • binom_cdf
    Enter your answer here
  • binom_info
    Enter your answer here
  • binom_inv
    Enter your answer here
  • binom_pdf
    Enter your answer here
  • normal
    Enter your answer here
  • normal_inv
    Enter your answer here
  • z_score
    Enter your answer here
  • confidence
    Enter your answer here
  • margin
    Enter your answer here
  • p_binom_info
    Enter your answer here
  • p_small_info
    Enter your answer here
  • discrete_info
    Enter your answer here
  • continuous_info
    Enter your answer here
  • Public Library: [methods_intcalc]
  • area_2curves
    Input: area_2curves("function 1","function 2","variable","lower bound","upper bound") Example: area_2curves(sin(x),cos(x),x,0,5) Summary: - Finds the total bounded area between 2 curves. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • function_avg
    Input: function_avg("function","variable","lower bound","upper bound") Example: function_avg(log(x),x,1,2) Summary: Finds the average value of a function over an interval.
  • riemann_sum
    Input: riemann_sum("function","variable","lower bound","upper bound") Example: riemann_sum(sin(x),x,0,pi/2) Summary: - Finds the approximated area under a curve using the left endpoint rule. or: - Finds the approximated area under a curve using the right endpoint rule. or: - Finds the approximated area under a curve using the midpoint rule. and: - Finds the actual area under the curve. Notes: *Enter number of partitions desired in the chosen interval *Enter "1", "2" or "3" to choose type of approximation.
  • total_area
    Input: total_area("function","variable","lower bound","upper bound") Example: total_area(cos(2x),x,0,5) Summary: - Finds the total bounded area between a function and the horizontal axis. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • Public Library: [methods_prblty]
  • binom_cdf
    Input: binom_cdf() Summary: Finds the binomial CDF probability. (Probability over a range of values) Notes: Enter parameters as prompted.
  • binom_info
    Input: binom_info() Summary: - Finds mean, variance and standard deviation of a binomial probability distribution. - Calculates all possible probability values of the distribution. Notes: Enter parameters as prompted. *To generate a probability table, enter "1" when prompted. *Entering a 0 will not generate a probability table.
  • binom_inv
    Input: binom_inv() Summary: Finds the minimum number of trials required to meet a certain probability. Notes:
  • binom_pdf
    Input: binom_pdf() Summary: Finds the binomial PDF probability. (Probability at a given value) Notes: Enter parameters as prompted.
  • normal
    Input: normal() Summary: Finds the probabilities of a normal distribution. Notes: Enter parameters as prompted.
  • normal_inv
    Input: normal_inv() Summary: Finds the value for which a specific probability occurs. Notes: Enter parameters as prompted.
  • normal_solve (unreleased)
    Input: normal_solve() Summary: - Finds the mean given specified parameters and probabilities. or: - Finds the standard deviation given specified parameters and probabilities. or: - Finds both the mean and standard deviation given known probabilities. Notes: Enter parameters as prompted.
  • z_score
    Input: z_score() Summary: Standardises probability values on a normal distrubution. Notes: Enter parameters as prompted.
  • discrete_info
    Input: discrete_info({"sample space"},{"associated probabilities"} Example: discrete_info({1,2,3},{0.3,0.6,0.1}) *where: Pr(1)=0.3 , Pr(2)=0.6 , Pr(3) = 0.1 Summary: - Generates the probability distribution table - Finds the mean, variance and standard deviation of the probability distribution. Notes: The program will automatically check for the conditions for which: - The sum of all probabilities is 1 - Both lists have the same dimension Otherwise, the program will terminate.
  • continuous_info
    Input: continuous_info("probability density function",{"lower bound","upper bound"} Example: continuous_info(x,{0,sqrt(2)}) Summary: - Finds the mean, variance and standard deviation of the probability distribution. - Finds the 1st and 3rd quartiles, interquartile range, and median. Notes: *Function must be in terms of "x" *Hybrid functions can be used (lower and upper bounds still need to be specified) The program will automatically check for the conditions for which: - The function in its given domain is a probability density function Otherwise, the program will terminate.
  • Public Library: [methods_stats]
    not in this study design :(
  • confidence
    Enter your answer here
  • margin
    Enter your answer here
  • p_binom_info
    Enter your answer here
  • p_small_info
    Enter your answer here
  • Public Library: [methods_misc]
  • discriminant
    Input: discriminant("quadratic","variable") Example: discriminant(x^2-x+2,x) Summary: - Calculates the discriminant of a quadratic. or: - Finds when the quadratic has two, one or no solutions. Example 2: discriminant(x^2+c,x) *Choose [2] to "solve discriminant". *Enter the unknown variable (in this case, "c") *Choose [1],[2] or [3] to select disriminant condition.
  • linear_info
    Input: linear_info([x1,y1],[x2,y2]) or: linear_info([x,y],"gradient") Example: linear_info([1,2],[5,6]) or: linear_info([1,2],4) Summary: - Finds the midpoint, distance and gradient between 2 points. - Finds the equation of the linear line between 2 points and its axial intercepts. or: - Finds the equation of a linear line and its axial intercepts, given a point and a gradient. Notes: *Square brackets must be used to represent coordinate pairs.
  • normal_line_w
    Input: normal_line_w("function","variable","point") Example: normal_line_w(sin(x),x,3) Summary: Shows all working out to obtain the normal line.
  • tangent_line_w
    Input: tangent_line_w("function","variable","point") Example: tangent_line_w(sin(x),x,3) Summary: Shows all working out to obtain the tangent line.
  • system_solve (unreleased)
    Input: sytem_solve("coefficient matrix","output vector","variable") Example: Summary: Calculates when a linear system of 2 variables has a unique solution, no solution or infinitely many solutions. How to enter a matrix: - Method 1: Use the template menu on the left of the catalogue button. - Method 2: Press [menu][7][1][1] - Method 3: Press [shift][return] to add column or [return] to add a row inside of a square bracket.
  • Public Library: [methods_func]
  • analyse
    Input: analyse("function","variable") Example: analyse(sin(x),x) Summary: - Maximal domain - Axial intercepts - Derivative - Stationary points and their nature - Inflection points and their nature Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • function_inv
    Input: function_inv("function of x") Example: function_inv(x^2) Summary: Finds the inverse function. Notes: Function must be in terms of "x"
  • intersect
    Input:​​ intesect("function 1","function 2","variable") Example: intersect(x/2,3log(x),x) Summary: Finds the intersection points (x&y coordinates) of 2 functions. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • transform
    Input: transform("function","variable") Example: transform(sin(x),x) Summary: Functionality #1: Applying transformations to functions: - Finds the image of a function under given transformations - Finds the transformation matrix Functionality #2: - Finds the transformations between two functions given a base function - Finds the transformation matrix - Lists the transformations Functionality #3: - Finds the pre-image of a function under known transformations - Finds the transformation matrix Functionality #4: - Finds the image of a point under given transformations
  • Public Library: [methods_diffcalc]
  • avg_change
    Input:​​ avg_change("function","variable","lower bound","upper bound") Example: avg_change(log(x),x,3,7) Summary: Finds the average rate of change of a function between two endpoints. Notes: Uses the formula (f(b)-f(a))/(b-a) to calculate the average change.
  • function_grad
    Input:​ ​ function_grad("function","variable","point") Example: function_grad(x^2,x,3) - {finds gradient of x^2 at x=3) Summary: Finds the gradient of a function at a point.
  • function_lines
    Input:​​ function_lines("function","variable","point") Example: function_lines(sin(x),x,pi) Summary: - Finds the derivative. - Finds equation of the tangent line at a point. - Finds axial intercepts of the tangent line. - Finds equation of the normal line at a point. - Finds the axial intercepts of the normal line.
  • normal_line
    Input:​​ normal_line("function","variable","point") Example: normal_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient and perpendicular gradient. - Finds the axial intercepts of the normal line. - Finds the equation of the normal line.
  • solve_grad
    Input:​​ solve_grad("function","variable","gradient") Example: solve_grad(tan(x),x,1) Summary: - Finds the derivative - Finds the points (x&y coordinates) on the function with the specified gradient. Notes: *To obtain approximated answers, press [ctrl][enter] to run the program. *When prompted to add a domain: - Enter "1" to add a domain. - Enter "0" to set domain to R. If a "1" is entered, you will be prompted again to add domain endpoints.
  • analyse
    Enter your answer here
  • tangent_line
    Input:​​ tangent_line("function","variable","point") Example: tangent_line(cos(x),x,3) Summary: - Finds the derivative - Finds the gradient. - Finds the axial intercepts of the tangent line. - Finds the equation of the tangent line.
  • function_inv
    Enter your answer here
  • intersect
    Enter your answer here
  • transform
    Enter your answer here
  • area_2curves
    Enter your answer here
  • function_avg
    Enter your answer here
  • riemann_sum
    Enter your answer here
  • total_area
    Enter your answer here
  • discriminant
    Enter your answer here
  • linear_info
    Enter your answer here
  • normal_line_w
    Enter your answer here
  • tangent_line_w
    Enter your answer here
  • system_solve (in development)
    Enter your answer here
  • binom_cdf
    Enter your answer here
  • binom_info
    Enter your answer here
  • binom_inv
    Enter your answer here
  • binom_pdf
    Enter your answer here
  • normal
    Enter your answer here
  • normal_inv
    Enter your answer here
  • z_score
    Enter your answer here
  • confidence
    Enter your answer here
  • margin
    Enter your answer here
  • p_binom_info
    Enter your answer here
  • p_small_info
    Enter your answer here
  • discrete_info
    Enter your answer here
  • continuous_info
    Enter your answer here
  • Public Library: [methods_intcalc]
  • area_2curves
    Input: area_2curves("function 1","function 2","variable","lower bound","upper bound") Example: area_2curves(sin(x),cos(x),x,0,5) Summary: - Finds the total bounded area between 2 curves. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • function_avg
    Input: function_avg("function","variable","lower bound","upper bound") Example: function_avg(log(x),x,1,2) Summary: Finds the average value of a function over an interval.
  • riemann_sum
    Input: riemann_sum("function","variable","lower bound","upper bound") Example: riemann_sum(sin(x),x,0,pi/2) Summary: - Finds the approximated area under a curve using the left endpoint rule. or: - Finds the approximated area under a curve using the right endpoint rule. or: - Finds the approximated area under a curve using the midpoint rule. and: - Finds the actual area under the curve. Notes: *Enter number of partitions desired in the chosen interval *Enter "1", "2" or "3" to choose type of approximation.
  • total_area
    Input: total_area("function","variable","lower bound","upper bound") Example: total_area(cos(2x),x,0,5) Summary: - Finds the total bounded area between a function and the horizontal axis. - Finds the total bounded area of each partition of the total area. - Finds the lower and upper bounds for each integral.
  • Public Library: [methods_prblty]
  • binom_cdf
    Input: binom_cdf() Summary: Finds the binomial CDF probability. (Probability over a range of values) Notes: Enter parameters as prompted.
  • binom_info
    Input: binom_info() Summary: - Finds mean, variance and standard deviation of a binomial probability distribution. - Calculates all possible probability values of the distribution. Notes: Enter parameters as prompted. *To generate a probability table, enter "1" when prompted. *Entering a 0 will not generate a probability table.
  • binom_inv
    Input: binom_inv() Summary: Finds the minimum number of trials required to meet a certain probability. Notes:
  • binom_pdf
    Input: binom_pdf() Summary: Finds the binomial PDF probability. (Probability at a given value) Notes: Enter parameters as prompted.
  • normal
    Input: normal() Summary: Finds the probabilities of a normal distribution. Notes: Enter parameters as prompted.
  • normal_inv
    Input: normal_inv() Summary: Finds the value for which a specific probability occurs. Notes: Enter parameters as prompted.
  • normal_solve (unreleased)
    Input: normal_solve() Summary: - Finds the mean given specified parameters and probabilities. or: - Finds the standard deviation given specified parameters and probabilities. or: - Finds both the mean and standard deviation given known probabilities. Notes: Enter parameters as prompted.
  • z_score
    Input: z_score() Summary: Standardises probability values on a normal distrubution. Notes: Enter parameters as prompted.
  • discrete_info
    Input: discrete_info({"sample space"},{"associated probabilities"} Example: discrete_info({1,2,3},{0.3,0.6,0.1}) *where: Pr(1)=0.3 , Pr(2)=0.6 , Pr(3) = 0.1 Summary: - Generates the probability distribution table - Finds the mean, variance and standard deviation of the probability distribution. Notes: The program will automatically check for the conditions for which: - The sum of all probabilities is 1 - Both lists have the same dimension Otherwise, the program will terminate.
  • continuous_info
    Input: continuous_info("probability density function",{"lower bound","upper bound"} Example: continuous_info(x,{0,sqrt(2)}) Summary: - Finds the mean, variance and standard deviation of the probability distribution. - Finds the 1st and 3rd quartiles, interquartile range, and median. Notes: *Function must be in terms of "x" *Hybrid functions can be used (lower and upper bounds still need to be specified) The program will automatically check for the conditions for which: - The function in its given domain is a probability density function Otherwise, the program will terminate.
  • Public Library: [methods_stats]
    not in this study design :(
  • confidence
    Enter your answer here
  • margin
    Enter your answer here
  • p_binom_info
    Enter your answer here
  • p_small_info
    Enter your answer here
  • Public Library: [methods_misc]
  • discriminant
    Input: discriminant("quadratic","variable") Example: discriminant(x^2-x+2,x) Summary: - Calculates the discriminant of a quadratic. or: - Finds when the quadratic has two, one or no solutions. Example 2: discriminant(x^2+c,x) *Choose [2] to "solve discriminant". *Enter the unknown variable (in this case, "c") *Choose [1],[2] or [3] to select disriminant condition.
  • linear_info
    Input: linear_info([x1,y1],[x2,y2]) or: linear_info([x,y],"gradient") Example: linear_info([1,2],[5,6]) or: linear_info([1,2],4) Summary: - Finds the midpoint, distance and gradient between 2 points. - Finds the equation of the linear line between 2 points and its axial intercepts. or: - Finds the equation of a linear line and its axial intercepts, given a point and a gradient. Notes: *Square brackets must be used to represent coordinate pairs.
  • normal_line_w
    Input: normal_line_w("function","variable","point") Example: normal_line_w(sin(x),x,3) Summary: Shows all working out to obtain the normal line.
  • tangent_line_w
    Input: tangent_line_w("function","variable","point") Example: tangent_line_w(sin(x),x,3) Summary: Shows all working out to obtain the tangent line.
  • system_solve (unreleased)
    Input: sytem_solve("coefficient matrix","output vector","variable") Example: Summary: Calculates when a linear system of 2 variables has a unique solution, no solution or infinitely many solutions. How to enter a matrix: - Method 1: Use the template menu on the left of the catalogue button. - Method 2: Press [menu][7][1][1] - Method 3: Press [shift][return] to add column or [return] to add a row inside of a square bracket.
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