CHI-SQUARE TESTS, INFERENCES FOR REGRESSION, AND ANOVAChi-square goodness-of-fit testAutoWrecks, Inc. sells auto insurance. AutoWrecks keeps close tabs on its customers' driving records, updating its rates according to the trends indicated bythese records. AutoWrecks' records indicate that, in a "typical" year, roughly 70% of the company's customers do not commit a moving violation, 10% commitexactly one moving violation, 15% commit exactly two moving violations, and 5% commit three or more moving violations.This past year's driving records for a random sample of 100 AutoWrecks customers are summarized by the first row of numbers in the table below. That rowgives this year's observed frequency for each moving violation category for the sample of 100 AutoWrecks customers. The second row of numbers gives thefrequencies expected for a sample of 100 AutoWrecks customers if the moving violations distribution for this year is the same as the distribution for a "typical"year. The bottom row of numbers contains the following value for each of the moving violation categories.(fo-ƒE)²ƒEPart 10/5(Observed frequency - Expected frequency)²Expected frequencyFill in the missing values in the table. Round your responses for the expected frequencies to two or more decimal places. Round yourto three or more decimal places.(Jo-12)²SEresponsesTiff ObservedfrequencyfoExpectedfrequencySE2(fo-fe)²ƒEPart 2Noviolations5500Exactly oneviolation1310.000.900Exactly twoviolations2615.008.067(a) Determine the type of test statistic to use.Type of test statistic: (Choose one)(b) Find the value of the test statistic. (Round your answer to two or more decimal places.)Three or moreviolations(c) Find the critical value. (Round your answer to two or more decimal places.)06(d) Can we reject the hypothesis that there is no difference between this year's moving violationdistribution and the distribution in a "typical" year?O Yes O No00Answer the following to summarize the test of the hypothesis that there is no difference between this year's moving violation distributidistribution in a "typical" year. Use the 0.05 level of significance for the test.TotalX100

Chi square is the type of parametric test. It used to test whether there is variable is independent…

Answered: AutoWrecks, Inc. sells auto insurance.…

College Algebra

10th Edition

ISBN:9781337282291

Author:Ron Larson

Publisher:Ron Larson

Chapter3: Polynomial Functions

Section3.5: Mathematical Modeling And Variation

Problem 2ECP

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Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation Based on a significance level of 5%, which of the following independent variables significantly predict the cash price of wheat? a. Economic Growth b. Population Growth c. Meat Consumption d. All the independent variables significantly predict the cash price of wheat.
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. The actual cash price of wheat under investigation in 1963 was $2.13. Based on the comparison of the correct predicted cash price calculated in the previous question and the actual cash price, what does the evidence suggest about Cargill, Inc.? a. Because the predicted price is relatively close to the actual price (within one cent), Cargill, Inc. probably did not artificially manipulate the price of wheat.…
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. In 1963, during the period in question, economic growth was 3.8; population growth was 1.40; and meat consumption was 152.95. Based on these values, what would be the predicted cash price of wheat at this time in 1963?
Managing Ashland MultiComm Services In its continuing study of the 3-For-All subscription solicitation process, a marketing department team wants to test the effects of two types of structured sales presentations (personal formal and personal informal) and the number of hours spent on telemarketing on the number of new subscriptions. The staff has recorded these data for the past 24 weeks AMS14. Analyze these data and develop a multiple regression model to predict the number of new subscriptions for a week, based on the number of hours spent on telemarketing and the sales presentation type. Write a report, giving detailed findings concerning the regression model used.
In simple linear regression, most often we perform a two-tail test of the population slope 1 to determine whether there is sufficient evidence to infer that a linear relationship exists. The null hypothesis is stated as:
In a simple regression analysis for a given data set, if the null hypothesis β = 0 is rejected, then the null hypothesis ρ = 0 is also rejected. This statement is ___________ true.
32. What effect on the results of a regression does data that exhibits heteroscedasticity cause? Higher risk of type I error Lower risk of type I error Higher risk of type II error
Which of the following is not an example of systematic error in an observational study? A cross-sectional study recruits participants that are willing to sign up outside of a major university and meet the inclusion and exclusion criteria to take part in the survey relating unsafe sex habits to STIs. A researcher is interested in the relationship between coffee drinking and lung cancer, and after careful multivariate linear regression modeling determines that a significant percentage of the relationship is due to another variable, cigarette smoking. An observational study recruits participants for a study looking at Alzheimer’s disease due to exposure to industrial hazards by asking participants to recall their exposure over the past 10 years. data-entry specialist responsible for adding in fasting glucose levels to a database accidentally skipped an observation during the input phase of data cleaning.
Management at Jagoda Wholesalers, in Calgary, Canada, has used time-series regression based on point-of-sale data to forecast sales for the next 4 quarters. Sales estimates are $100,000, $120,000, $140,000, and $160,000 for the respective quarters. Seasonal indices for the four quarters have been found to be1.30, .90, .70, and 1.10, respectively.
(a) In regression analysis, often the researcher will encounter issues of omitted variable bias (OVB) or their included variables are too closely related (multicollinearity). In your own words, (i) explain what is meant by OVB? (ii) what is multicollinearity?(iii) How do these problems lead to type1/type 2 errors? (b) In your own words, describe your understanding of linear regression analysis. What is the causal fallacy? (c) How is the model fit measured? In your answer describe both the R-squared and SER. (d) What are dummy variables and how are they interpreted? (e) As an economist you are interested in diminishing returns. Describe how you would go about modelling this.
The administration of a midwestern university commissioned a salary equity study to help establish benchmarks for faculty salaries. The administration utilized the following regression model for annual salary, y : ?(?) β0+β1x ,where ?=0 if lecturer, 1 if assistant professor, 2 if associate professor, and 3 if full professor. The administration wanted to use the model to compare the mean salaries of professors in the different ranks. a) Explain the flaw in the model. b)Propose an alternative model that will achieve the administration’s objective. c) If the global F-test for the model you proposed in 2 is conducted, what would be the value of the numerator degrees of freedom?
Which of the following statements about a least-squares regression analysis is true?I. A point with a large residual is an outlier.II. A point with high leverage has a -value that is not consistent with the other -values in the set.III. The removal of an influential point from a data set could change the value of the correlation coefficient.

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