Financial_Modeling_Midterm_13

# If yes on which side provide a justification for your

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Unformatted text preview: considered for test Chi2 distribution in this case? f) Knowing that the χ2 value is 6.25 and its p-value is 4.39%, what we can conclude about the statistical significance and the strength of the relation? g) Which are the managerial conclusions of this survey? 3 3) LINEAR CORRELATION ANALYSIS Like all firms, life insurance companies continually seek new ways to deliver products to the market. Those involved in product development want to know who buys insurance and how much they buy. Analysts can readily get information on characteristics of current customers through company databases. Potential customers, those who do not have insurance with the company, are often the main focus for expanding market share. We consider here a survey on a sample of n = 275 families that purchased term life insurance. The analysis aims at determining family characteristics that influence the amount of insurance purchased. The variables under scrutiny are: è༎ • FACE amount that the company will pay in the event of the death of the named insured, • INCOME è༎ annual income of the respondent, • EDUCATION è༎ number of years of education of the survey respondent, • NUMHH è༎ number of household members. Using the JMP outputs attached below, answer to the following questions: a) Is the FACE distribution skewed? If yes, on which side? Provide a justification for your answer. b) Which variables are most highly correlated with FACE? c) Would you reject the null hypothesis that the population correlation coefficient between FACE and NUMHH is zero? Explain briefly (use an α = 5%). d) Would you trust in the results reported in attachment? Why? 4 Distributions FACE Quantiles 14000000 Summary Statistics 100.0% maximum 1e+7 99.5% 9190600 97.5% 1020000 90.0% 295200 75.0% quartile 120000 50.0% median 65000 25.0% quartile 38000 10.0% 20000 2.5% 4000 0.5% 260 0.0% minimum 260 Mean Std Dev Std Err Mean Upper 95% Mean Lower 95% Mean N Quantiles 4000000 8000000 Mean Std Dev Std Err Mean Upper 95% Mean Lower 95% Mean N Quantiles 0 Summary Statistics 100.0% maximum 1.4e+7 99.5% 1.25e+7 97.5% 5325000 90.0% 2000000 75.0% quartile 600000 50.0% median 150000 25.0% quartile 50000 10.0% 10000 2.5% 4900 0.5% 1446 0.0% minimum 800 Summary Statistics 747581.45 1674362.4 100967.85 946...
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