A city claims that of households recycle regularly. A researcher surveys households to see if the true proportion is different and finds that recycle regularly. Use to test the claim.
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
9. Hypothesis Testing for One Sample
Performing Hypothesis Tests: Proportions
Problem 8.5.3b
Textbook Question
At Least As Extreme A random sample of 860 births in New York State included 426 boys, and that sample is to be used for a test of the common belief that the proportion of male births in the population is equal to 0.512.
b. For random samples of size 860, what sample proportions of male births are at least as extreme as the sample proportion of 426/860?
Verified step by step guidance1
Calculate the sample proportion of male births using the formula: . This will give the observed sample proportion.
Determine the null hypothesis proportion, which is given as 0.512. This represents the assumed proportion of male births in the population under the null hypothesis.
Compute the standard error (SE) of the sample proportion using the formula: , where is the null hypothesis proportion (0.512) and is the sample size (860).
Calculate the z-score for the observed sample proportion using the formula: , where is the observed sample proportion, is the null hypothesis proportion, and is the standard error.
Find the z-scores that correspond to sample proportions at least as extreme as the observed proportion. This involves considering both tails of the distribution (greater than the observed proportion and less than the symmetric lower proportion). Use a z-table or statistical software to determine the corresponding probabilities.
Verified video answer for a similar problem:This video solution was recommended by our tutors as helpful for the problem above
Video duration:
3mPlay a video:
0 Comments
Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Sample Proportion
The sample proportion is the ratio of the number of successes (in this case, male births) to the total number of observations in the sample. It is calculated by dividing the number of boys (426) by the total number of births (860), yielding a sample proportion of 0.495. This value is crucial for hypothesis testing as it serves as the basis for comparing against the hypothesized population proportion.
Recommended video:
Sampling Distribution of Sample Proportion
Hypothesis Testing
Hypothesis testing is a statistical method used to determine whether there is enough evidence in a sample to infer that a certain condition holds for the entire population. In this context, the null hypothesis states that the proportion of male births is equal to 0.512. The test will evaluate if the observed sample proportion is significantly different from this hypothesized value, using a significance level to make a decision.
Recommended video:
Guided course
Step 1: Write Hypotheses
At Least As Extreme
The phrase 'at least as extreme' refers to the values of the sample proportion that are as far away from the hypothesized population proportion as the observed sample proportion. This concept is used to determine the critical values for the hypothesis test, which helps in assessing whether the observed sample proportion is statistically significant compared to the expected proportion under the null hypothesis.
Recommended video:
Guided course
Comparing Mean vs. Median
Related Videos
Related Practice
Multiple Choice
48
views
