Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 56m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 17m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - ExcelBonus23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - ExcelBonus28m
- Confidence Intervals for Population Means - ExcelBonus25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - ExcelBonus42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - ExcelBonus27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - ExcelBonus28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - ExcelBonus12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - ExcelBonus9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - ExcelBonus21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - ExcelBonus12m
- Two Variances and F Distribution29m
- Two Variances - Graphing CalculatorBonus16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - ExcelBonus8m
- Finding Residuals and Creating Residual Plots - ExcelBonus11m
- Inferences for Slope31m
- Enabling Data Analysis ToolpakBonus1m
- Regression Readout of the Data Analysis Toolpak - ExcelBonus21m
- Prediction Intervals13m
- Prediction Intervals - ExcelBonus19m
- Multiple Regression - ExcelBonus29m
- Quadratic Regression15m
- Quadratic Regression - ExcelBonus10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 29m
4. Probability
Counting
Problem 5.5.52
Textbook Question
Simple Random Sample How many different simple random samples of size 7 can be obtained from a population whose size is 100?
Verified step by step guidance1
Identify the problem as a combination problem because the order of selection does not matter in a simple random sample.
Recall the formula for combinations, which is used to find the number of ways to choose \(k\) items from \(n\) items without regard to order:
\[ C(n, k) = \frac{n!}{k! (n-k)!} \]
Substitute the given values into the formula: here, \(n = 100\) (population size) and \(k = 7\) (sample size). So, the expression becomes
\[ C(100, 7) = \frac{100!}{7! (100-7)!} \]
Understand that \$100!\( (100 factorial) means the product of all positive integers from 1 to 100, and similarly for \)7!\( and \)93!$; these factorials can be simplified to make calculation easier.
Use a calculator or software capable of handling large factorials to compute the value of \(C(100, 7)\), which will give the total number of different simple random samples of size 7 from a population of 100.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Simple Random Sample
A simple random sample is a subset of individuals chosen from a larger population where each member has an equal chance of being selected. This method ensures unbiased representation and is fundamental for valid statistical inference.
Recommended video:
Simple Random Sampling
Combination Formula
The combination formula calculates the number of ways to choose a subset of items from a larger set without regard to order. It is given by nCr = n! / [r!(n - r)!], where n is the population size and r is the sample size.
Recommended video:
Combinations
Factorial Notation
Factorial notation (n!) represents the product of all positive integers up to n. It is essential for computing combinations and permutations, as it helps quantify the total arrangements or selections possible.
Recommended video:
Introduction to Permutations
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