International Journal of Statistical Distributions (Vol. 1, 2015) study of a variable life insurance policy, Exercise 4.97 (p. 239). Recall that a ratio (x) of the rates of return on the investment for two consecutive years was shown to have a normal distribution, with μ = 1.5 and o= .2. Consider a random sample of 100 variable life insurance policies and let represent the mean ratio for the sample. a. Find E(T) and interpret its value.
International Journal of Statistical Distributions (Vol. 1, 2015) study of a variable life insurance policy, Exercise 4.97 (p. 239). Recall that a ratio (x) of the rates of return on the investment for two consecutive years was shown to have a normal distribution, with μ = 1.5 and o= .2. Consider a random sample of 100 variable life insurance policies and let represent the mean ratio for the sample. a. Find E(T) and interpret its value.
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Chapter1: Starting With Matlab
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Please do a, e, and f only

Transcribed Image Text:## Statistical Analysis of Variable Life Insurance Return Rates
### Study Overview
This exercise references a study from the *International Journal of Statistical Distributions* (Vol. 1, 2015) which focuses on variable life insurance policies. The task is derived from Exercise 4.97 (p. 239).
### Problem Summary
We are examining the ratio (\(x\)) of the rates of return on investments for two consecutive years. Previous studies indicate that this ratio follows a normal distribution with:
- Mean (\(\mu\)) = 1.5
- Standard deviation (\(\sigma\)) = 0.2
A random sample of 100 variable life insurance policies is considered, with \(\bar{x}\) representing the mean ratio for the sample.
### Tasks
a. **Find \(E(\bar{x})\) and Interpret**
b. **Find Var(\(\bar{x}\))**
c. **Describe the Shape of the Sampling Distribution of \(\bar{x}\)**
d. **Calculate the z-score for \(\bar{x} = 1.52\)**
e. **Determine \(P(\bar{x} > 1.52)\)**
f. **Assess Impact of Non-Normal Distribution on Previous Answers**
### Problem Analysis
1. **Expectation of \(\bar{x}\) (\(E(\bar{x})\)):**
- Since the sample mean is an unbiased estimator of the population mean, \(E(\bar{x}) = \mu = 1.5\).
2. **Variance of \(\bar{x}\) (Var(\(\bar{x}\)):**
- The variance of the sample mean is given by Var(\(\bar{x}\)) = \(\sigma^2/n\) = \(0.2^2/100 = 0.0004\).
3. **Shape of the Sampling Distribution:**
- The Central Limit Theorem states that for a sufficiently large sample size, the sampling distribution of the sample mean \(\bar{x}\) will be approximately normal. Given the sample size of 100, the distribution of \(\bar{x}\) is normal.
4. **Z-score Calculation:**
- Z = \((1.52 - 1.5) / (0.2/\sqrt{100}) = 1\).
5. **Probability \(P(\bar{x} > 1.52)\):**
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