A time series regression equation measuring the number of surfboards sold by a surfboard manufacturing company in Australia is given below: Y = 35 + 4Q1+0.5Q3 + 8Q4 + 3t with t in quarters and the first observation of the dataset is December 2015 and Q1 is the indicator variable for March, Q3 is the indicator variable for September and Q4 is the indicator variable for December. Which of the following statements is correct? There is a mistake with this regression model because all the coefficients of the indicator variables are positive. There is no indicator variable for the June quarter in this model because there is no seasonal effect in June due to winter in Australia. There is a mistake with this model because the indicator variable for the June quarter has been left out. There is no indicator variable for the June quarter because the model has an intercept and including the June quarter would result in perfect multicollinearity.

A time series regression equation measuring the number of surfboards sold by a surfboard manufacturing company in Australia is given below: Y = 35 + 4Q1+0.5Q3 + 8Q4 + 3t with t in quarters and the first observation of the dataset is December 2015 and Q1 is the indicator variable for March, Q3 is the indicator variable for September and Q4 is the indicator variable for December. Which of the following statements is correct? There is a mistake with this regression model because all the coefficients of the indicator variables are positive. There is no indicator variable for the June quarter in this model because there is no seasonal effect in June due to winter in Australia. There is a mistake with this model because the indicator variable for the June quarter has been left out. There is no indicator variable for the June quarter because the model has an intercept and including the June quarter would result in perfect multicollinearity.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Question: Does the scatterplot show evidence of a positive or negative linear relationship between the unemployment rate and the number of violent crimes?
According to an article, one may be able to predict an individual's level of support for ecology based on demographic and ideological characteristics. The multiple regression model proposed by the authors was the following. y = 3.60-.01x₁+.01.₂-.07x3+.12x4+.02xs-.04x6-01-.04.xg-.02.xg+c The variables are defined as follows. y = ecology score (higher values indicate a greater concern for ecology) X₁ = age times 10 x₂ = income (in thousands of dollars) x3 = gender (1 = male, 0 = female) X4 = race (1 = white, 0 = nonwhite) X5 = education (in years) x6 = ideology (4 = conservative, 3 = right of center, 2 = middle of the road, 1 = left of center, and 0 = liberal) X7 = social class (4 = upper, 3 = upper middle, 2 = middle, 1 = lower middle, 0 = lower) xg = postmaterialist (1 if postmaterialist, 0 otherwise) x9 = materialist (1 if materialist, 0 otherwise) (a) Suppose you knew a person with the following characteristics: a 30 year old, white female with a college degree (20 years of…
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.5 8.4 6.8 3.4 2.5 2.6 2.6 0.9 13.5 10.9 9.8 6.7 6.3 6.4 4.5 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation 0.83x + 3.99. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 8.4 thousand automatic weapons? Answer Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 7.3 thousand automatic weapons? Answer Round to 3 decimal places.
Select the most appropriate response. If the correlation between a person’s age and annual income is 0.60, then the coefficient of determination tells us that: 36% of the variation in a person’s annual income can be explained by the predictor variable age. 36% of a person’s annual income can be explained by their age 60% of the variation in a person’s annual income can be explained by the predictor variable age 60% of a person’s annual income can be explained by their age
I. Show complete solutions. Simple Linear Regression and Correlation: a.) Results of a study on the occurrence of sodium and chloride in surface streams in a coastal area. The following data are chloride concentration (y) in mg/liter and road-way area in the watershed (x) in %. Y 0.24 4.6 1. What is the value of the correlation coefficient? 2. What is the interpretation for this case? 3. What is the estimated chloride concentration if road way 0.3 6.4 0.45 9.8 area in the watershed is 1.30% 0.75 10.7 4. What is the equation of the prediction line? 0.57 10.6 0.75 10.9 0.4 11.7 0.8 12.5 0.6 14 0.78 14.7 0.85 15.1 0.68 18.3 0.69 19.2 1.4 23.1 1.15 27.3 1.05 27.7 1.69 32 1.62 38.4
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 biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? d) Suppose the correlation coefficient was −0.12. How would you classify the correlation between temperature and amount of glucose?
When attempting to predict job performance using personality traits, researchers typically assume that the relationship is linear. A study investigated a curvilinear relationship between job task performance and a specific personality trait-conscientiousness. Using data collected for 602 employees of a large public organization, task performance was measured on a 30-point scale (where higher scores indicate better performance) and conscientiousness was measured on a scale of -3 to +3 (where higher scores indicate a higher level of conscientiousness). 1. The coefficient of correlation relating task performance score to conscientiousness score was reported as r = 0.18. Explain why the researchers should not use this statistic to investigate the curvilinear relationship %D between task performance and conscientiousness. 2. Give the equation of a curvilinear (quadratic) model relating task performance score (y) to conscientiousness Score (x). 3. The researchers theorized that task…
Below is the output from regression. Use the p-value and decide if the equation is good to use for forecasting. Group of answer choices A.) p-value = 82 - equation is good to use B.) p-value = 0.15 - equation is not good to use C.) p-value = 0.18 - equation is not good to use D.) p-value = -16 - equation is good to use