8.1.2.1.4 - Example: Overweight Citizens.8.1.2.1 - Normal Approximation Method Formulas.8.1.1.3 - Computing Necessary Sample Size.

8.1.1.2.2 - Example with Summarized Data.

8.1.1.2.1 - Example with Summarized Data.

8.1.1.2 - Minitab: Confidence Interval for a Proportion.

8.1.1.1.2 - Video Example: Dog Ownership.

8.1.1.1.1 - Video Example: PA Residency.

8.1.1.1 - Normal Approximation Formulas.

7.4.2.3 - Example: 99% CI for Proportion of Women Students.

7.4.2.2 - Video Example: 90% CI for the Correlation between Height and Weight.

7.4.2.1 - Video Example: 98% CI for Mean Atlanta Commute Time.

7.4.1.6 - Example: Difference in Mean Commute Times.

7.4.1.4 - Example: Proportion of Women Students.

7.4.1.3 - Example: Proportion NFL Coin Toss Wins.

7.4.1.2 - Video Example: Correlation Between Printer Price and PPM.

7.4.1.1 - Video Example: Mean Body Temperature.

7.3 - Minitab: Finding Values Given Proportions.

7.2.3.1 - Example: Proportion Between z -2 and +2.

7.2 - Minitab: Finding Proportions Under a Normal Distribution.6.6 - Confidence Intervals & Hypothesis Testing.5.5.4 - Correlation Example: Quiz & Exam Scores.5.5.3 - Difference in Means Example: Exercise by Biological Sex.5.5.1 - Single Proportion Example: PA Residency.5.5 - Randomization Test Examples in StatKey.5.3.1 - StatKey Randomization Methods (Optional).5.1 - Introduction to Hypothesis Testing.4.6 - Impact of Sample Size on Confidence Intervals.

4.4.2.2 - Example: Difference in Dieting by Biological Sex.

4.4.2.1 - Example: Correlation Between Quiz & Exam Scores.

4.4.1.2 - Example: Difference in Mean Commute Times.

4.4.1.1 - Example: Proportion of Lactose Intolerant German Adults.

4.3.2 - Example: Bootstrap Distribution for Difference in Mean Exercise.

4.3.1 - Example: Bootstrap Distribution for Proportion of Peanuts.

4.2.1 - Interpreting Confidence Intervals.

4.2 - Introduction to Confidence Intervals.

4.1.1.2 - Coin Flipping (One Proportion).

3.5 - Relations between Multiple Variables.

3.4.2.2 - Example of Computing r by Hand (Optional).

3.4.2.1 - Formulas for Computing Pearson's r.

3.3 - One Quantitative and One Categorical Variable.

2.2.6 - Minitab: Central Tendency & Variability.

2.2.1 - Graphs: Dotplots and Histograms.

2.1.3.2.5.1 - Advanced Conditional Probability Applications.

2.1.3.2.1 - Disjoint & Independent Events.

2.1.2.1 - Minitab: Two-Way Contingency Table.

1.2.2.1 - Minitab: Simple Random Sampling.

1.1.2 - Explanatory & Response Variables.

1.1.1 - Categorical & Quantitative Variables.

The third column of your worksheet should now be labeled 'MySample' and it should contain 10 names.В Since we are using simple random sampling procedures, the results will be different each time due to random sampling variation.В Try these steps a few times, you should see that you get a different set of 10 names each time.

Click in the Store samples inВ box andВ type My Sample.

Click in the From columnsВ box and then double click the NameВ variable.

In the Number of rows to sampleВ box, enter 10.

From the tool bar, select Calc >В Sample from Columns.

At the bottom of this section there is a video that shows where to click. To select a simple random sample of 10 names from this dataset, follow the steps below. After the first time, you computer should recognize that. You should be able to fix this by saving the file to your desktop, opening Minitab, and then opening the worksheet from within Minitab. mwxВ file you may receive an error message if your computer does not know to open this in Minitab. xlsxВ is an Excel file this can be opened withВ any version of Minitab as well as with Excel: mwxВ is a Minitab worksheet file this can only be opened with Minitab 20. In this example, we have a worksheet containingВ the names of all of the Department of Statistics' full-time faculty members from the Spring 2021 semester.

Minitab express generate random numbers