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Texas Instruments TI-83 Plus Manual

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TI-83, TI-83 Plus and the TI-84
GRAPHING CALCULATOR MANUAL
James A. Condor
Manatee Community College
to accompany
Introductory Statistics
Sixth Edition
by
Prem S. Mann
Eastern Connecticut State University
JOHN WILEY & SONS, INC.

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  • Page 1 TI-83, TI-83 Plus and the TI-84 GRAPHING CALCULATOR MANUAL James A. Condor Manatee Community College to accompany Introductory Statistics Sixth Edition Prem S. Mann Eastern Connecticut State University JOHN WILEY & SONS, INC.
  • Page 2 Contents Preface Introduction Organizing Data Numerical Descriptive Measures Probability Discrete Random Variables Continuous Random Variables Sampling Distributions Estimation of the Mean and Proportion Hypothesis Tests: Mean and Proportion 10 Two Populations 11 Chi-Square Tests 12 Analysis of Variance 13 Simple Linear Regression 14 Nonparametric Methods...

  • Page 3: Preface

    John Wiley & Sons, Inc., but should also prove useful with other texts. It will not explain the underlying statistics but instead focus on how best to use the TI-83, TI-83 Plus, and TI-84 Plus calculators in computing them.

  • Page 4: Introduction

    Chapter Introduction Use of Technology Statistics is a field that deals with sets of data. After the data is collected it needs to be organized and interpreted. There is a limit to how much of the work can be done effectively without the help of some type of technology.
  • Page 5 Advantages to Using the TI-83, TI-83 Plus, and TI-84 Plus This calculator manual will focus on how to get the most out of using the TI-83, TI-83 Plus, and the TI-84 Plus calculators by Texas Instruments. The TI-83 was first released in 1996, improving upon its predecessors the TI-81 and TI-82 with the addition of many advanced statistical and financial functions.
  • Page 6 Press the STAT key. To input data or to make changes to an existing set of data values use the Edit function. (number one under the EDIT list). Press the number 1 key. If your stat list editor does not show the columns labeled L1, L2, and L3 you can set up the Editor by selecting SetUpEditor on the previous screen.
  • Page 7 Use the up and down arrow keys to go back and forth between the numbers. Try changing the value of one of the entries by typing in a new weight. Clearing a List of Data Values After a list of data values is no longer needed you can delete the values by using one of the following methods.
  • Page 8 On the other hand, you cannot just type the name of a named list from the keyboard using the ALPHA key. List names on the TI-83, TI-83 Plus, and TI-84 Plus calculators are distinguished from the names of other variables by a small L to the left of the name. To see this, go into the...
  • Page 9 If that should happen go into the MEM menu (above the + key) and follow the sub-menus for deleting items from your calculator. This is one of the few places where the steps differ between the TI-83, TI-83 Plus, and TI-84 Plus calculators. Just follow the instructions on the screen.
  • Page 10 Exercises 1. Store the following numbers into the list L2: 11, 23, 35, 47, 59 2. Store the following numbers into the list ABC: 2, 3, 5, 7, 11, 13 3. Configure the Stat List Editor to display lists L2, ABC, and L5. 4.

  • Page 11: Organizing Data

    One of the simplest ways of organizing data is to group together similar values. Once we’ve grouped them together we can count how many elements there are in each group. Since the TI- 83, TI-83 Plus, and TI-84 plus calculators work only with numerical data, we will be trying to group numbers together.
  • Page 12 Team Home Runs Team Home Runs Anaheim Milwaukee Arizona Minnesota Atlanta Montreal Baltimore New York Mets Boston New York Yankees Chicago Cubs Oakland Chicago White Sox Philadelphia Cincinnati Pittsburgh Cleveland St. Louis Colorado San Diego Detroit San Francisco Florida Seattle Houston Tampa Bay Kansas City...
  • Page 13 Press the GRAPH key. Press the TRACE key. Use the arrow keys to move from one bar to the other. (this will supply information on the frequency in each category and the range of values in each category). The histogram provides us with the following information. _Class_ Frequency 124-145...
  • Page 14 We’re going to construct a frequency histogram from the data. The data ranges from 15.4 to 31.2 so we will have our classes go from 15 to 33. That is a range of 33 − 15 = 18, so we will construct six classes of width 3.
  • Page 15 Creating a Dotplot You can create a Dotplot by using the scatterplot option under STATPLOT. The steps to create the dotplot are very similar to those needed to create the histogram. We will use the data stored under the label WORK that we used to create the histogram in the previous example. Press the STAT key.
  • Page 16 Exercises 1. Take the following statistics exam scores and construct a frequency histogram for them with classes from 50 to 59, from 60 to 69, from 70 to 79, from 80 to 89, and from 90 to 99. 2. Create a dot plot using the above data values. Solutions 1.

  • Page 17: Numerical Descriptive Measures

    After learning about percentiles, another visual display can be created called the box-and-whisker plot. On the TI-83, TI-83 Plus, and TI-84 Plus calculators, many numerical descriptive statistics for one variable are gathered together into one command called 1-Var Stats. There are two ways of using this command: with ungrouped data and with grouped data.
  • Page 18 Example: Prices of CDs. The following data values are the prices of the same popular CD sold from ten different discount stores. 12.95, 14.90, 11.57, 14.65, 17.95, 21.25, 12.95, 20.35, 10.95, 25.05 Press the STAT key. Press the number 1 key. Enter the data values into L1.
  • Page 19 The last five numbers (minimum, first quartile, median, third quartile, and maximum) are collectively known as the five-number summary of the data. From the five-number-summary we can compute the range, which is the difference between maxX and minX, and the interquartile range, which is the difference between Q3 and Q1.
  • Page 20 The first line displays the sample mean, Ë = 78 million dollars. The fourth line displays the sample standard deviation, Sx = 34.4 million dollars. Use the down arrow key to see that the median payroll is 75 million dollars. The interquartile range is found by subtracting the two quartiles Q3−Q1 = 109.5 −...
  • Page 21 Grouped Data Sometimes a set of data values has many numbers that show up over and over again. Instead of typing those numbers in over and over again you can save time by typing in the numbers that repeat along with how many times it repeats. Example: Multiple Frequencies The following is a list of data values that are ungrouped.
  • Page 22 Measures of Position Percentiles The TI-83, TI-83 Plus, and TI-84 Plus do not perform percentile calculations directly, but do simplify them tremendously by helping us to sort data with the command SortD( . Once the data is sorted, the kth percentile is found by counting up to the kth position in the sorted data and dividing by the number of data values in the data set.
  • Page 23 Elementary Statistics Final Exam Scores Enter the exam scores into a list with the name FINAL. Press the STAT key. Press the number 1 key. Press the ▲ key to highlight L1. Press the 2 key and then the DEL key to get to INS. Type in FINAL for the name of the new list.
  • Page 24 Box-and-Whisker Plot The TI-83, TI-83 Plus, and TI-84 Plus calculators will take a list of data and automatically draw a box-and-whisker plot for that data. Since there are a number of different types of plots available on the calculator, it is important to make sure that all other plots are turned off before you begin or you graph will be cluttered with several unrelated plots being graphed at the same time.
  • Page 25 Set the Freq: to 1. Press the ZOOM key. Press the number 9 key. We have two different types of box-and-whisker plots to select from. One will separate outliers from the maximum or minimum value and the other will include the outliers in to whiskers. If you select the picture that shows two dots after the maximum on the plot, the outliers will be shown outside of the whiskers.
  • Page 26 Exercises 1. Find the mean and sample standard deviation for the following data: 34 23 55 91 23 34 12 34 98 23 2. Find the median and interquartile range for the following grouped data: Score Frequency 3. What is the percentile rank of 82 in the following data? 4.

  • Page 27: Probability

    Chapter Probability Generating Random Numbers When working with probabilities, it is sometimes useful to generate numbers that you can’t predict, but at the same time follow some standard rules. Computer simulations are a common example of the need for random occurrences within a structured setting. These numbers are called pseudo-random numbers since they are not totally random.
  • Page 28 Press the ENTER key. Press the ENTER key again. If you continue to press the ENTER key you will generate a different random number between zero and one each time you press the ENTER key. Generating Random Numbers Between any Two Values If you would like to generate random real numbers that are equally likely to occur and fall within a specified range of values you can use the rand function with some additional commands.
  • Page 29 (1,100,20)→L1. Generating Other Kinds of Real Numbers There are two other functions built into the TI-83, TI-83 Plus, and TI-84 Plus calculators to generate random numbers. There are many situations in statistics where you need to generate numbers from distributions where the numbers are not equally likely to occur. Two of the most commonly used distributions used in statistics are the normal and the binomial.
  • Page 30 The syntax for these functions are similar to those needed for the randInt( function. For example, if you wanted to generate 30 numbers from a normal distribution with a mean of 45 and a standard deviation of 8 and store them in L2 you would select number 6 and type in randNorm(45, 8, 30)→L2.
  • Page 31 Exercises 1. Create a random integer value between 50 and 60 inclusive. 2. Create a random real number between 3 and 17. 3. Generate 15 random integer values between 1 and 50 and store them in L1. 4. Select 10 values from a normally distributed population with a mean of 22 and a standard deviation or 2.6.

  • Page 32: Discrete Random Variables

    For example: Value (x) Probability P(x) The TI-83, TI-83 Plus, and TI-84 Plus calculators allow you to input the values and their probabilities into separate lists and use the 1-Var Stats function to compute the descriptive statistics.
  • Page 33 Press the STAT key. Press the number 1 key. Enter the x values into L1. Enter the P(x) probabilities into L2. Press the STAT key. Press the ► key to highlight CALC. Press the number 1 key When 1-Var Stats appears on the calculator screen, type in L1, L2 to get 1-Var Stats L1, L2. After you press the ENTER key you will get the descriptive statistics which includes the population mean and standard deviation.
  • Page 34 Permutations Another common function needed to compute dependent probabilities that uses factorial is the permutation formula. The number of permutations is normally written as nPr; on the calculator with the symbol P written between n and r, where n is the total number of elements, and r is the number being selected.
  • Page 35 Binomial Probabilities The command for computing binomial probabilities is binompdf( , which is located on the DISTR page (which is found in yellow above the fourth key in the fourth row). To find the probability of x successes out of n trials, each with probability p of success, type binompdf(n, p, x).
  • Page 36 Find the probability that at most 2 or less of these 10 packages will not arrive at its destination within the specified time. Press the 2 key and then press the VARS key to get to the DISTR page. Press the ALPHA key and then press the MATH key to get to the letter A which is the binomcdf( function.
  • Page 37 Example: New Bank Accounts Suppose that on the average two new accounts per day are opened at an Imperial Savings Branch bank. Let’s find the probability that on a given day at least 7 new accounts are opened. The complement of opening at least 7 new accounts is opening at most 6 new accounts, which has probability poissoncdf(2, 6) = 0.9955.
  • Page 38 Exercises 1. Find 13! 2. Find the number of ways to deal a five-card hand from a deck of 52 cards. 3. Find the number of four-digit numbers that don’t have any digits repeated. 4. A company has 50 fork-lifts. On any given day each fork-lift has a 1% chance of needing maintenance.

  • Page 39: Continuous Random Variables

    Traditionally normal distribution probabilities were figured using a normal distribution table. The table method is being replaced with calculators such as the TI-83, TI-83 Plus, and TI-84 Plus. The calculator reduces the time needed to perform the calculations and reduces the rounding errors that occur because of the brevity of the tables in elementary statistics textbooks.
  • Page 40 Press the 2 key and then the VARS key to get to the DISTR page. Press the number 1 key. Type in 1, 0, 1) to get normalpdf(1,0,1). Press the ENTER to get .242. To find the probability of getting a value that falls within a range of values from the standard normal distribution you can use the normalcdf( function which stands for normal cumulative density function.
  • Page 41 Press the number 2 key. Type in the number -1. Press the 2 key and then the , (comma) key to get the exponential sign E. Type in 99, 0, 1) after the E. Press the ENTER key. The probability is .159. Example: Finding the Area to the Right To find the area to the right of a number a, type normalcdf(a, 1E99).
  • Page 42 Example: Finding the Area Between two values. To find the area between two numbers a and b, type normalcdf(a, b, 0, 1). Find the probability of getting a value between 1.04 and 1.82 on the standard normal distribution. Press the 2 key and then the VARS key to get to the DISTR page.
  • Page 43 Find the probability of getting a score less than 32 given the distribution is normally distributed with a mean of 45 and a standard deviation of 12. Using the normalcdf( function and typing in -1E99, then the score 32 with a mean of 45 and standard deviation of 12 we get the probability .139.
  • Page 44 To find the score that is associated with the lowest 1% of the area under the normal distribution we use invNorm( . Press the 2 key and then the VARS key to get to the DISTR page. Press the number 3 key. Type in .01, 54, 8).
  • Page 45 Exercises 1. What is the probability that a z-score will lie between 2 and 3? 2. How likely is it for a z-score to be over 2.5? 3. What is the chance that a z-score is less than 1.3? 4. The heights in inches at a certain age are normally distributed with mean 48 and standard deviation 3.2.

  • Page 46: Sampling Distributions

    Ë Ë To find the probability that a < Ë < b on the TI-83, TI-83 Plus, and TI-84 Plus calculators, use normalcdf(a, b, µ, σ/ ). The procedure is the same as finding the probability of a single value except for the standard deviation being divided by the square root of the sample size.
  • Page 47 Press the 2 key and then press the VARS key to get to the DISTR page. Press the number 2 key. Type in -1E99, 31.8, 32, .3/ 20 )) Press the ENTER key. The probability is .0014. Example: Tuition According to a recent College report, the average tuition and fees at four year private colleges and universities in the United States is $18,273 for the academic year 2002–2003.
  • Page 48 Probabilities for Sample Proportions For a large sample size, we know from the Central Limit Theorem that the sampling distribution pq / for ê is normally distributed with µ = p and σ ê ê To find the probability that a < ê < b on the calculator, use normalcdf(a, b, p, pq / ).
  • Page 49 Exercises 1. Assume that the average annual family income in a given city is $28,000 with standard deviation $3200. If a random sample of fifty families is taken, what is the chance that the average income of the fifty families will be over $29,000? 2.

  • Page 50: Estimation Of The Mean And Proportion

    Chapter Estimation of the Mean and Proportion In statistics we collect samples to find things out about a population. If the sample is representative of the population, the sample mean or proportion should be statistically close to the actual population mean or proportion. A way to judge how close the sample statistic may be is to create a confidence interval.
  • Page 51 Press the STAT key. Press the ► twice to highlight TESTS. Press the number 7 key. The first line under ZInterval has two options for Inpt: Data Stats. If the mean and standard deviation are given in the problem then Stats should be selected. If the data values are given in the problem then Data should be selected.
  • Page 52 We do not have the population standard deviation σ, so we will use TInterval. Press the STAT key. Press the ► key twice to highlight TESTS. Press the number 8 key. We do not have the data itself, so we will select Stats. We enter 15,528 for Ë, 4200 for s , 400 for n, and .99 for the C-Level.
  • Page 53 Example: Legal Advice According to a 2002 survey by FundLaw, 20% of Americans needed legal advice during the past year to resolve such thorny issues as trusts and landlord disputes. Suppose a recent sample of 1000 adult Americans showed that 20% of them needed legal advice in the past year to resolve such family-related issues.
  • Page 54 Exercises 1. Given that σ = 4 for a given population and the following sample data, find a 95% confidence interval for µ. 24 31 31 34 18 22 2. Find a 90% confidence interval for µ given the following sample data from a normal population: 87 97 67 88 97 73 81 3.
  • Page 55 Hypothesis tests about means can be Z-based (if σ is known) or T-based (if σ is unknown and either the population is normal or the sample size is over 30). The TI-83, TI-83 Plus, and the TI- 84 Plus calculators provide functions for both the Z-Test and the T-Test. They both provide a p- value for comparison with the test’s significance level.
  • Page 56 Press the STAT key. Press the ► key twice to highlight TESTS. Press the number 2 key. Move the cursor over Stats and press the ENTER key. Type in 50352 for µ Type in 51750 for Ë. Type in 5240 for Sx. Type in 200 for n.
  • Page 57 Press the STAT key. Press the ► key twice to highlight TESTS. Press the number 2 key. Move the cursor over Stats and press the ENTER key. Type in 65 for µ Type in 63 for Ë. Type in 2 for Sx. Type in 15 for n.
  • Page 58 defective chips. Test at the 5% significance level whether or not the machine needs an adjustment. Our hypotheses are H : p = 4% and H : p > 4%. We will use 1-PropZTest, with p = 0.04, x = 14, n = 200, and p >...
  • Page 59 Press the STAT key. Press the ► key twice to highlight TESTS. Press the number 5 key. Type in .9 for p Type in 129 for x. Type in 150 for n. Move the cursor over <P and press the ENTER key. Move the cursor over Calculate and press the ENTER key.
  • Page 60 Exercises 1. Test at a 5% significance level whether or not µ = 98 given the following sample data from a normal population: 2. Test at a 5% significance level whether or not p is more than 75%, given a sample proportion of 80% and a sample size of 235.

  • Page 61: Two Populations

    Chapter Estimation and Hypothesis Testing: Two Populations Confidence Interval for µ1 − µ2 If you are fortunate enough to have information about the population standard deviations, you can use 2-SampZInt to estimate the difference µ1 − µ2 between two population means. The most general approach, though, to estimating the difference of two population means is to compute a T-based confidence interval, which does not make any assumptions about the population’s standard deviations.
  • Page 62 Press the STAT key. Press the ► key twice to highlight TESTS. Press the number 9 key. Move the cursor over Stats and press the ENTER key. Type in 9000 for σ1 Type in 8500 for σ2. Type in 49056 for Ë1. Type in 500 for n1.
  • Page 63 Example: Average Salaries Test at the 1% significance level if the 2001 mean salaries of full-time state employees in New York and Massachusetts are different. We are testing to choose between H : µ1 = µ2 and H : µ1 ø µ2. Since we have the population standard deviations, we will use 2-SampZTest.
  • Page 64 Pretest/Posttest. Data is collected from a sample before some type of treatment and then data is collected again from that same sample after the treatment. We then work with the mean difference between the pre and posttest scores. The null hypothesis is that the average difference is zero.
  • Page 65 The T-Test output shows the: alternative hypothesis: µ>0 test statistic: t=1.226498265 p-value: p=.1329778401 sample mean: Ë=5 sample standard deviation: Sx=10.78579312 sample size: n=7 The p-value is greater than the significance level so we accept the null hypothesis of no significant difference between the pre and posttest scores. There is not enough evidence to claim that the special dietary plan was effective in lowering systolic blood pressure.
  • Page 66 Enter in the data for the 2-PropZTest. Highlight Calculate and press the ENTER key. The test statistics is 1.15 and the p-value is .13. Since the p-value is larger than the significance level of 1% we accept the null hypothesis. There is not enough evidence to claim significant differences between toothpaste loyalties.
  • Page 67 Exercises 1. Given the following sample data from two populations, find a 95% confidence interval for µ − µ = 23.5 = 17.9 Ë Ë = 5.1 = 2.4 = 38 = 59 2. Given the following paired data, test at a 5% significance level whether or not µ = µ...
  • Page 68 Solutions 1. Since we do not have the population standard deviations, we will use a T-based confidence interval, 2-SampTInt, and select Stats. We enter the sample statistics and choose No for the Pooled prompt since we do not know that the population standard deviations are equal. The confidence interval is (3.82, 7.38).

  • Page 69: Chi-Square Tests

    The computed expected frequencies are then stored automatically in another matrix. The TI-83, TI-83 Plus, and the older models of the TI-84 Plus calculators will not perform the Chi-Square Goodness-of-Fit test. More recently-manufactured TI-84 Plus calculators have a GOF-Test function on the STAT page and in the TESTS list.
  • Page 70 Test at a 5% significance level whether or not Gender and Opinion are independent. Then check the expected frequencies for any possible practical significant differences between the associated observed and expected frequencies. First the data has to be stored differently than in previous statistical test on the calculator. The data for a Chi-Square test of independence has to be stored in a Matrix.
  • Page 71 Since the p-value of .016 is less than the significance level of .05 we reject the null hypothesis that row and column variables are independent. We have evidence that gender does influence opinions concerning school discipline. The see the expected frequencies Press the 2 key and then the x key.
  • Page 72 After MATRIX [A] type in 2 x2. The 2 represents the number of rows in the table. The 2 represents the number of columns in the table. Press the ENTER key. Enter the data values into the matrix as they appear in the table.
  • Page 73 Exercises 1. Test at a 5% significance level whether or not state and blood type are independent of each other using the following sample data. Type O Type A Type B Type AB New York California 2. Test at a 5% significance level whether or not car ownership and cell phone ownership are independent of each other using the following sample data.

  • Page 74: Analysis Of Variance

    Chapter Analysis of Variance One-Way Analysis of Variance We have already seen how to test for the equality of means between two different populations with 2-SampZTest and 2-SampTTest. With the added assumption of common population standard deviations, we can extend the tests to more than two populations with a technique known as Analysis of Variance (ANOVA for short).
  • Page 75 Press the STAT key. Press the ENTER key to get to the Stat Editor. Enter the Method I data values into L1. Enter the Method II data values into L2. Enter the Method III data values into L3. Press the STAT key. Press the ►...
  • Page 76 Teller A Teller B Teller C Teller D At a 5% level of significance, test the null hypothesis that the mean number of customers served per hour by each of the four tellers is the same. Assume all the assumptions required to apply the one-way ANOVA procedure hold true.
  • Page 77 Exercises 1. Test at a 5% significance level whether or not these samples come from populations with identical means. Assume that the populations are all normally distributed with identical standard deviations. Solutions 1. The p-value is .779. Accept the null hypothesis. The population means are not significantly different from each other.

  • Page 78: Simple Linear Regression

    Chapter Simple Linear Regression Simple Linear Regression Models A simple linear regression model is an equation describing how to use one variable x to predict another y, based on the relationship that we see in sample data. Since we are making predictions that may differ from the actual values, we use the symbol y' for the predicted value of y.
  • Page 79 Creating a Linear Regression Model The TI-83, TI-83 Plus, and the TI-84 Plus calculators have the function LinReg on the STAT page in the CALC list and will compute a simple linear regression model. Note there are two forms, one that writes the equation as ax+b and the other that writes the equation as a+bx.
  • Page 80 Press the STAT key. Press the ► key. Press the number 8 key. Press the 2 key and then the number 1 key to get L1. Press the , (comma) key. Press the 2 key and then the number 2 key to get L2. Press the , (comma) key.
  • Page 81 Hypothesis Tests Just because we can compute a regression model does not mean that it is a valid model. We can test whether or not the variable x can meaningfully predict the variable y by running LinRegTTest to test whether or not B, the population slope coefficient for the model (approximated by b) is really 0.
  • Page 82 Type in L1, L2, Y1. Press the ENTER key. The LinReg output shows the: general model: y=a+bx y-intercept: a=1.142245989 slope: b=.264171123 coefficient of determination: r =.9190178504 linear correlation coefficient: r=.9586541871 We perform the test of H : B = 0 versus H : B >...
  • Page 83 Exercises Use the following sample data for the exercises in this chapter. Height (in) Weight (lbs) 1. Compute a regression model for this data and also find the coefficient of determination and the linear correlation coefficient. 2. Use the model to estimate the weight of someone who is 5’8”, i.e., 68 inches tall. 3.

  • Page 84: Nonparametric Methods

    Chapter Nonparametric Methods Sign Test The Sign Test is one of the most direct nonparametric methods; knowing nothing about the distribution, we can still test claims about the median of a population because any sample measurement stands a 50% chance of being above the median and a 50% chance of being below the median.
  • Page 85 Home Price 1 147,500 2 123,600 3 139,000 4 168,200 5 129,450 6 132,400 7 156,400 8 188,210 9 198,425 10 215,300 Using a 5% significance level, can we conclude that the median price differs from $137,000? Our hypotheses are H0 : the median price is $137,000 versus H1 : the median price differs from $137,000.
  • Page 86 Our hypotheses are H0 : the median is at least $70 versus H1 : the median is less than $70. For a large sample size (n = 89 households that were above or below $70) we can use 1-PropZTest. The p-value is 0.084, which is greater than our significance level. We fail to reject the null hypothesis, and believe that the median price is at least $70.
  • Page 87 Since the p-value is less than our significance level 2.5%, we reject the null hypothesis. The data leads us to conclude that the diet does lower the median systolic blood pressure of adults. Example: Math Anxiety Many students suffer from math anxiety. A statistics professor offered her students a two-hour lecture on math anxiety and ways to overcome it.
  • Page 88 Index 1-Var Stats, 13 analysis of variance, 65 ANOVA, 65 binomial distribution, 28 binomial probabilities, 28 box-and-whisker plot, 20 Central Limit Theorem, 39 combinations, 28 confidence interval, 43 confidence interval for µ, 43 confidence interval for p, 47 contingency tables, 59 creating lists, 4 cumulative binomial probability, 29 data entry, 2...
  • Page 89 minimum, 14 naming lists, 4 nonparametric methods, 75 normal distribution, 34 normal probabilities, 34 numbers, pseudo-random, 23 numbers, random, 23 observed frequencies, 59 percentiles, 19 plot, box-and-whisker, 20 Poisson distribution, 30 Poisson probabilities, 30 population standard deviation, 14 probabilities, sample mean, 39 probabilities, sample proportion, 40 pseudo-random numbers, 23 quartile, first, 14...

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