A human resources department is comparing two employee training programs to see if they lead to different pass rates on a required certification exam. They randomly select two groups of employees. In Program A, 16 out of 20 employees passed the exam. In Program B, 30 out of 40 employees passed. Are the basic conditions met to conduct a 2-proportion hypothesis test?
Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 2m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 27m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - ExcelBonus23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- 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 20m
- 9. Hypothesis Testing for One Sample5h 15m
- Steps in Hypothesis Testing1h 13m
- Performing Hypothesis Tests: Means1h 1m
- Hypothesis Testing: Means - ExcelBonus42m
- Performing Hypothesis Tests: Proportions39m
- Hypothesis Testing: Proportions - ExcelBonus27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions29m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 35m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - ExcelBonus28m
- Two Means - Unknown, Unequal Variance1h 2m
- 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 CalculatorBonus15m
- 11. Correlation1h 24m
- 12. Regression3h 42m
- 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 Slope32m
- Enabling Data Analysis ToolpakBonus1m
- Regression Readout of the Data Analysis Toolpak - ExcelBonus21m
- Prediction Intervals13m
- Prediction Intervals - ExcelBonus19m
- Multiple Regression - ExcelBonus29m
- Quadratic Regression23m
- Quadratic Regression - ExcelBonus10m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 33m
10. Hypothesis Testing for Two Samples
Two Proportions
Multiple Choice
A school administrator wants to compare the proportion of students who passed a standardized math exam in two different schools by taking samples from 2 classes. Assume the samples are random and independent. Calculate the z-score for testing whether there is a significant difference in the population proportions of student passing rates, but do not find a P-value or draw a conclusion for the hypothesis test.
Class A: 72 out of 120 students passed.
Class B: 65 out of 100 students passed.
A
-0.76
B
1.07
C
-1.07
D
0.76
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Verified step by step guidance1
Step 1: Define the problem and set up the hypothesis test. The null hypothesis (H₀) is that the population proportions of students passing the exam in the two schools are equal (p₁ = p₂). The alternative hypothesis (H₁) is that the population proportions are not equal (p₁ ≠ p₂).
Step 2: Calculate the sample proportions for each class. For Class A, the proportion is p̂₁ = 72 / 120. For Class B, the proportion is p̂₂ = 65 / 100.
Step 3: Compute the pooled proportion (p̂) using the formula: p̂ = (x₁ + x₂) / (n₁ + n₂), where x₁ and x₂ are the number of successes (students who passed) in each class, and n₁ and n₂ are the sample sizes of each class.
Step 4: Calculate the standard error (SE) for the difference in proportions using the formula: SE = √[p̂(1 - p̂)(1/n₁ + 1/n₂)].
Step 5: Compute the z-score using the formula: z = (p̂₁ - p̂₂) / SE. This z-score will indicate whether there is a significant difference in the population proportions of students passing rates between the two schools.
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