PSYC 354 Excel Homework Points Possible Module Week 6 Assign

PSYC 354 Excel Homework pts possible) Module/Week 6’s assignment has two parts

The assignment involves constructing confidence intervals and conducting hypothesis tests in Excel based on known population parameters. It is divided into two parts: Part One requires constructing a 95% confidence interval for the population mean of minutes exercised per week, and Part Two involves testing a hypothesis about whether freshmen exercise less than the general population. The data provided includes raw data for 50 freshmen. Students will fill in cells with known information, compute sample means, alpha levels, confidence intervals, and conduct hypothesis tests with appropriate formulas, as instructed in the course presentation. After performing calculations, students will answer related questions directly in the Excel file. The exercise aims to develop proficiency in using Excel functions such as AVERAGE, CONFIDENCE, NORMSINV, and NORMSDIST to perform statistical analyses.

Paper For Above instruction

The primary goal of this assignment is to familiarize students with the process of constructing confidence intervals and conducting hypothesis tests using Excel, specifically when the population parameters are known. This exercise specifically centers around a research scenario involving students' weekly exercise hours and the myth that freshmen tend to gain weight due to decreased physical activity. By engaging in this exercise, students learn to integrate statistical concepts with practical Excel skills, fostering a deeper understanding of inferential statistics.

Part One of the assignment involves creating a 95% confidence interval for the true average minutes exercised per week for freshmen students. The known parameters from previous research include a population mean (μ) of 100 minutes and a standard deviation (σ) of 25 minutes. The sample of 50 freshmen provides raw data for calculating the sample mean. Students begin by inputting known information such as sample size and population standard deviation into designated cells. Using Excel’s AVERAGE function, the sample mean is computed from the raw data. They then determine the alpha level for a 95% confidence level, which is 0.05. The CONFIDENCE function in Excel is employed next to calculate the margin of error, from which the lower and upper bounds of the confidence interval are derived by subtracting and adding the margin of error from the sample mean, respectively.

Students then respond to five questions related to the confidence interval, which measure their understanding of the results—such as interpreting the interval or implications for the population mean. This helps cement the concept that the confidence interval provides a range within which the true population mean is likely to fall with 95% certainty.

Part Two involves hypothesis testing to evaluate whether the freshmen exercise less than the general population mean of 100 minutes. The null hypothesis (H0) states that the population mean is equal to 100, while the alternative hypothesis (H1) posits that it is less than 100. This is a one-tailed test, aligning with the research question. Students fill in the known values: N, μ, and σ, then compute the standard error (σM) using the formula σ / √N. The sample mean is again calculated using the AVERAGE function from the raw data.

The test is conducted at an alpha level of 0.05, and students find the critical Z value using the NORMSINV function, considering the one-tailed nature of the test. The sample Z statistic is calculated by subtracting the hypothesized mean from the sample mean and dividing by the standard error. The critical p-value corresponding to the alpha level is also recorded. Students then compute the p-value based on the sample Z score using the NORMSDIST function. Based on the p-value and critical value, they decide whether to reject the null hypothesis and interpret their findings in relation to the myth about freshmen's exercise behavior.

This exercise emphasizes understanding the application of inferential statistical methods using Excel, including the importance of correctly formulating hypotheses, choosing appropriate test types, and interpreting the results. The instructions prepare students to analyze real-world data, draw valid conclusions, and develop critical thinking skills regarding statistical evidence and claims in health psychology research.

References

  • Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
  • Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the Practice of Statistics. Macmillan.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
  • Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences. Cengage Learning.
  • Urdan, T. (2017). Statistics in Plain English. Routledge.
  • Cain, M., & Thurlow, R. (2018). How to Use Excel for Statistics. Wiley.
  • Hinton, P. R. (2014). Statistics explained. Routledge.
  • Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594–604.
  • Norušis, M. (2012). SPSS Statistics 19 Brief Version. Pearson.
  • Wikipedia contributors. (2023). Confidence interval. Wikipedia. https://en.wikipedia.org/wiki/Confidence_interval

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