HW-Week 5- Multiple Regression AnalysisThe following are data on the ages and incomes of a random sample of 5 executivesworking for XYZ corporation and their academic achievement while in colleaeIncome (inthousand pesos)YAgeX1AcademicAchievementX281.773.31.52.03830464089.579.069.91.751.752.5032a. Fit an equation of the form y=Dbo + bix +b2x2 to the equation of the given datab. Use the equation obtained in (a) to estimate the average income of a 35-yearold exécutive with 1.25 academic achievement.

Solution-: We find, (a) Multiple regression model (b) Predict X1=35,X2=1.25

Answered: The following are data on the ages and…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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16)
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density35 35043 34053 33954 32155 310 Step 3 of 6 : Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.
A weight-loss program wants to test how well their program is working. The company selects a simple random sample of 51 individual that have been using their program for 15 months. For each individual person, the company records the individual's weight when they started the program 15 months ago as an x-value. The subject's current weight is recorded as a y-value. Therefore, a data point such as (205, 190) would be for a specific person and it would indicate that the individual started the program weighing 205 pounds and currently weighs 190 pounds. In other words, they lost 15 pounds. When the company performed a regression analysis, they found a correlation coefficient of r = 0.707. This clearly shows there is strong correlation, which got the company excited. However, when they showed their data to a statistics professor, the professor pointed out that correlation was not the right tool to show that their program was effective. Correlation will NOT show whether or not there is…
Which of the following is the equation of the relationship between the predictor variable (x) and the response variable (y)? A y = 25.707 + 0.9887xB y = 29.796 + 0.5897xC y = 25.707 + 0.5897xD y = 29.796 + 0.9887x
10) The top point in the scatter plot (6.5, 1459) is for Washington, DC. Find the predicted DC violent crime rate if x=6.5. Then find the residual for DC using the predicted violence crime rate and the actual violent crime rate (the actual crime rate is provided in the first sentence of this question). Prediction = Actual y-value = Residual =
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density35 35043 34053 33954 32155 310 Step 4 of 6 : Determine the value of the dependent variable yˆ at x=0.
2. I surveyed 150 adults in the U.S. and asked them how many hours of TV the watched on average per week. I then ran a regression of # of hours of TV on whether or not they were a college graduate (=1 if yes, =0 if no), their age in years, the number of children in their household, and whether or not they live a cold climate (=1 if latitude is greater than 41.2, =0 otherwise). The results fro the regression are shown in the table below. Estimate p-value College Graduate -0.8 0.003 Age 0.1 0.150 Number of Children 0.10 0.521 Cold Climate 1.8 0.047 Intercept -1.23 0.041 (a) What is the dependent variable in this regression? (b) What are the independent variable(s) in this regression? (c) What is the unit of analysis? (d) What is the sample size? (e) What is one binary variable used in this analysis? (f) What is one ratio variable used in this analysis? (g) What is the predicted number of TV hours watched by a 50 year old, colleg graduate, with no children at home who lives in Arizona…
Data was gathered on the amount of rainfall X (in inches) for the last 5 years to analyze if the amount of rainfall X (in inches) could be used to predict the acorn output Y (in tons) for a group of oak trees. Researchers expect the the acorn output to increase linearly with the increase in rainfall amount. That is, they are specifically interested in testing if the correlation between the rainfall and the acorn output is positive (i.e.: ) . When the necessary conditions are satisfied, under the null hypothesis, test statistic for above test Group of answer choices follows a t distribution with degrees of freedom (n-1) follows a chi-square distribution with degrees of freedom (n-1) follows a t distribution with degrees of freedom (n-2) follows a standard normal distribution. follows a chi-square distribution with degrees of freedom (n-2)
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
A random sample of 136 adults were asked to report the number of hours per week the spent on a computer and their number of years of education. The linear model equation below describes the relationship between the mean computers hours and years of education. computers == 9.12 ++ 0.8 ×× education Based on this linear model, which of the following statements is correct? a) An adult who has no years of education is expected to spend 9.12 hours per week on a computer. b) An adult who has 1 more year of education than another is expected to spend 9.12 more hours per week on a computer. c) An adult who spends 1 more hour per week on a computer than another is expected to have had 9.12 more years of education. d) An adult who spend zero hours per week on a computer is expected to have 9.12 years of education.
A researcher studied the relationship between the amount of carbohydrates (x) and the amount of salt (y) in different food products. sample of 36 food products yielded these calculations: r = -0.1245, ŷ=0.5545– 0.1636x According to the correlation, if a food contains more carbohydrates, is it likely to contain more salt or less salt? Why or why not?

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