A city government claims that no more than of households have solar panels. A researcher suspects the rate is actually higher and surveys households, finding that have solar panels. Test if there is evidence that more than of households have solar panels using .
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 8.5.3a
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.
a. In testing the common belief that the proportion of male babies is equal to 0.512, identify the values of p^ and p.
Verified step by step guidance1
Step 1: Understand the problem. The goal is to test the common belief that the proportion of male births in the population is equal to 0.512. Here, 'p' represents the hypothesized population proportion, and 'p^' (read as 'p-hat') represents the sample proportion.
Step 2: Identify the value of 'p'. The problem states that the common belief is that the proportion of male births in the population is 0.512. Therefore, 'p' = 0.512.
Step 3: Calculate the sample proportion 'p^'. The formula for 'p^' is: , where 'x' is the number of successes (male births) and 'n' is the total sample size. Here, x = 426 and n = 860.
Step 4: Substitute the values into the formula for 'p^'. Using the formula: . This will give the sample proportion of male births.
Step 5: Interpret the values. 'p' is the hypothesized proportion (0.512), and 'p^' is the sample proportion calculated from the data. These values will be used in further statistical tests, such as a hypothesis test for proportions, to determine if the observed sample proportion significantly differs from the hypothesized population proportion.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Sample Proportion (p^)
The sample proportion, denoted as p^ (p-hat), is the ratio of the number of successes (in this case, the number of boys born) to the total number of observations in the sample. It provides an estimate of the population proportion based on the sample data. For the given question, p^ can be calculated as 426 boys out of 860 total births, which helps in assessing the validity of the common belief about the population proportion.
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Sampling Distribution of Sample Proportion
Population Proportion (p)
The population proportion, denoted as p, represents the true proportion of a certain characteristic in the entire population. In this scenario, it is the hypothesized proportion of male births, which is stated to be 0.512. Understanding the difference between p and p^ is crucial for hypothesis testing, as it allows us to compare the sample data against the established belief about the population.
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Hypothesis Testing
Hypothesis testing is a statistical method used to determine whether there is enough evidence in a sample to support a specific claim about a population parameter. In this context, the null hypothesis (H0) would state that the population proportion of male births is equal to 0.512, while the alternative hypothesis (H1) would suggest that it is not. This process involves calculating test statistics and p-values to make informed decisions regarding the hypotheses.
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Step 1: Write Hypotheses
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