How To Estimate The Difference Between Yearly Income ✓ Solved
In Order To Estimate The Difference Between The Yearly Incomes Of Mark
In order to estimate the difference between the yearly incomes of marketing managers in the east and the west, the following information was gathered: East (n1= 40, x1= 72 (in $1000), s1= 6 (in $1,000)); West (n2= 45, x2= 78 (in $1000), s2= 8 (in $1000)); degrees of freedom (DF)= 80.
1. What is the point estimate for the difference between the two population means?
2. At 90% confidence, what is the margin of error?
3. What is the 90% confidence interval for the difference between the two population means?
4. Using P-value approaches, test whether the yearly average income of marketing managers in the East is significantly different from that in the West.
a) State the null and alternative hypotheses.
b) What is the value of the test statistic?
c) What is the critical value?
d) What is the P-value?
e) What is your conclusion regarding the null hypothesis?
Additionally, an entomologist writes in a scientific journal that claims fewer than 7 in ten thousand male fireflies are unable to produce light due to a genetic mutation. Use the parameter p, the true proportion of fireflies unable to produce light, for analysis.
Sample Paper For Above instruction
Introduction
Understanding the statistical differences between two populations is essential in various fields, including economics and biology. In this analysis, we examine the difference in yearly incomes between marketing managers in the East and West regions and test a scientific claim regarding firefly luminescence. Employing confidence intervals and hypothesis tests, this paper aims to interpret the data accurately and assess the significance of findings.
Comparison of Yearly Incomes of Marketing Managers in East and West
The data provided compares the annual incomes of marketing managers in two geographical regions—East and West. The sample size for East is 40, with a mean income of $72,000 and a standard deviation of $6,000. The West's sample comprises 45 managers, with a mean income of $78,000 and a standard deviation of $8,000. The goal is to estimate the difference between the two population means and assess if this difference is statistically significant.
1. Point Estimate of the Difference
The point estimate for the difference between the population means is calculated as the difference between the sample means:
\[
\hat{\mu}_1 - \hat{\mu}_2 = x_1 - x_2 = 72 - 78 = -6 \text{ (in $1000)}
\]
This indicates that, on average, marketing managers in the East earn $6,000 less than their Western counterparts.
2. Margin of Error at 90% Confidence
Calculating the margin of error involves identifying the critical t-value for 90% confidence with approximately 80 degrees of freedom. Using a t-distribution table, the critical value \( t_{0.05, 80} \approx 1.664 \). The standard error (SE) for the difference is:
\[
SE = \sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}} = \sqrt{\frac{6^2}{40} + \frac{8^2}{45}} = \sqrt{\frac{36}{40} + \frac{64}{45}} \approx \sqrt{0.9 + 1.422} \approx \sqrt{2.322} \approx 1.523
\]
The margin of error (ME):
\[
ME = t_{\alpha/2, DF} \times SE = 1.664 \times 1.523 \approx 2.535
\]
Thus, the margin of error is approximately $2,535.
3. 90% Confidence Interval for the Difference
The confidence interval is:
\[
(x_1 - x_2) \pm ME = -6 \pm 2.535
\]
which translates to:
\[
(-8.535, -3.465) \text{ in thousand dollars}
\]
This interval suggests we are 90% confident that the true difference in average yearly incomes between East and West lies between approximately $3,465 and $8,535, with the East earning less.
4. P-Value Approach to Hypothesis Testing
a) Null hypothesis \( H_0: \mu_1 - \mu_2 = 0 \)
b) Alternative hypothesis \( H_1: \mu_1 - \mu_2 \neq 0 \)
c) To compute the test statistic:
\[
t = \frac{(x_1 - x_2) - 0}{SE} = \frac{-6}{1.523} \approx -3.939
\]
d) The degrees of freedom are approximated as 80, and the critical t-value for a two-tailed test at \(\alpha = 0.10\) (or 90% confidence) is approximately ±1.664.
e) Using the t-distribution table or calculator, the P-value for \( t = -3.939 \) with 80 degrees of freedom is approximately 0.0002, which is less than 0.10.
Conclusion: Since \( p
Analysis of Fireflies' Luminescence
The entomologist claims that fewer than 7 in 10,000 male fireflies are unable to produce light owing to a genetic mutation. Let \( p \) represent the proportion of such fireflies. To test this claim, data from a sample of fireflies would be used to perform a one-proportion z-test.
The null and alternative hypotheses are:
\[
H_0: p \geq \frac{7}{10000} = 0.0007
\]
\[
H_1: p
\]
The test involves calculating the sample proportion and the z-statistic to determine whether the evidence supports the claim that fewer than 7 in 10,000 fireflies are unable to produce light.
Conclusion
The confidence interval analysis indicates a statistically significant income difference favoring the West region's marketing managers. The hypothesis test strongly suggests a genuine disparity exists, which could influence regional policy and economic planning. In the biological context, the investigation into fireflies' luminescent ability exemplifies the application of hypothesis testing in scientific research, providing insights into genetic mutations affecting phenotypic traits.
References
- Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge.
- Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
- Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis. Guilford Publications.
- Moore, D. S., & McCabe, G. P. (2014). Introduction to the Practice of Statistics. W.H. Freeman.
- Peck, R., & Olson, C. H. (2004). Introduction to Statistics and Data Analysis. Addison Wesley.
- Selvin, H. (1991). Statistical Analysis of Epidemiologic Data. Oxford University Press.
- Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.
- Zar, J. H. (2010). Biostatistical Analysis. Pearson.
- Johnson, R. A., & Wichern, D. W. (2019). Applied Multivariate Statistical Analysis. Pearson.
- Lehmann, E. L., & Romano, J. P. (2005). Testing Statistical Hypotheses. Springer.