A market analyst is estimating the average monthly spending on online subscriptions among young adults. The point estimate y for the average monthly spending is \$45.60, with a margin of error of \$3.20 at a confidence level. Write and interpret a Confidence Interval for the average monthly spending.
Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 2m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 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 32m
7. Sampling Distributions & Confidence Intervals: Mean
Introduction to Confidence Intervals
Multiple Choice
Which of the following analytical techniques is primarily used to estimate a population parameter based on sample data?
A
Confidence intervals
B
Hypothesis testing
C
Regression analysis
D
Time series forecasting
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Verified step by step guidance1
Step 1: Begin by understanding the concept of a population parameter. A population parameter is a numerical characteristic of a population, such as the mean or proportion, which we often aim to estimate using sample data.
Step 2: Review the purpose of confidence intervals. Confidence intervals are a statistical technique used to estimate a population parameter by providing a range of values within which the parameter is likely to fall, based on sample data.
Step 3: Compare confidence intervals with other techniques listed in the options. Hypothesis testing is used to test claims or assumptions about a population parameter, regression analysis is used to model relationships between variables, and time series forecasting is used to predict future values based on historical data.
Step 4: Recognize that confidence intervals are specifically designed to estimate population parameters, making them the correct analytical technique for this purpose.
Step 5: Conclude that confidence intervals are the most appropriate choice for estimating a population parameter based on sample data, as they provide a range of plausible values for the parameter.
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