The model Yị = Bo + B, In X;+e; was estimated using E-Views and the results are shown inthe following Table. What is the value of the slope at X = 10Dependent variable: YIncluded observations: 10VariableCoefficientStd. Errort-StatisticProb.53.266675.7704499.2309400.0000In (x)0.9299920.0075-3.303030R-squaredMean dependent var35.10000Adjusted R-squared..S.D. dependent var12.78410S.E. of regression...Residuals sum square..... ......F-statisticProb(F-statistic)0.007491

Here the regression equation is linear : where, Yi represents the predicted value of Y β0…

The model Y; Bo + B, In X;+e; was estimated using E-Views and the results are shown in the following Table. What is the value of the slope at X =10 Dependent variable: Y Included observations: 10 Variable Coefficient Std. Error t-Statistic Prob. C 53.26667 5.770449 9.230940 0.0000 In (x) 0.929992 0.0075 -3.303030 10000

The model Y; Bo + B, In X;+e; was estimated using E-Views and the results are shown in the following Table. What is the value of the slope at X =10 Dependent variable: Y Included observations: 10 Variable Coefficient Std. Error t-Statistic Prob. C 53.26667 5.770449 9.230940 0.0000 In (x) 0.929992 0.0075 -3.303030 10000

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

A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to the…
Which of the slopes matches best with the slope of the data in the scatter plot? would it be m = 3/8?, m = 8/3?, m = - 3/8? or would it be m = - 8/3???
1945 is paired with cancer rates for 1975. The cigarette consumption data is from 1945 to 1975 in 5-year increments. There is a strong positive linear association between annual cigarette consumption and lung cancer rates. The correlation is 0.81. If we use the line to make predictions of the number of lung cancer deaths per 100,000 people, which of the following is an example of extrapolation? a. Predict Y when cigarette consumption is 3,500 cigarettes per person. b. Predict Y when cigarette consumption is 3,800 cigarettes per person. c. Predict Y when cigarette consumption is 4,600 cigarettes per person.
The multiple regression describes how the mean value of y is related to the xi independent variables. The parameters ?i are used to describe how the mean value of y changes for a one-unit increase in xi when the other variables are held constant. The given estimated regression equation follows where x1 is the high-school grade point average, x2 is the SAT mathematics score, and y is the final college grade point average. ŷ = −1.38 + 0.0232x1 + 0.00482x2 If the variable x2 is held constant, then only changes in x1 will impact the predicted values of ŷ. Since the coefficient of x1 is positive, for each one-unit increase of x1, the values of ŷ will increase by the value of ?1, where ?1 = . In context, for each one point increase of the high-school grade point average, the final college grade point average will increase by this amount when the SAT mathematics score does not change. If the variable x1 is held constant, then only changes in x2 will impact the predicted values of ŷ. Since the…
For the following example indicate the type of data involved using the following: A = nominal data B = ordinal data C = interval data 1. Number of patients with coronary pathology in nursing homes in the U.S
From the graphs displayed, is there evidence to suggest that there is a positive linear relationship between Square Feet and Bathrooms, for the population of real estate represented by this sample? Be sure to provide numeric support in your answer.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.8 8.2 6.9 3.6 2.5 2.8 2.6 0.4 14 11.3 10.2 7.4 6.5 6.3 5.9 4.5 x thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equationý = 0.85x + 4.12. Use this equation to answer the following; %D A) How many murders per 100,000 residents can be expected in a state with 7.3 thousand automatic weapons? Answer = Nearest whole value. B) How many murders per 100,000 residents can be expected in a state with 1 thousand automatic weapons? Nearest whole value Answer =
The following table contains ACT scores and the GPA for eight college students. Estimate the relationship between GPA (yi) and ACT (x;) using OLS regression by hand. Report the intercept and slope estimates: Student 1 2 3 4 5 6 7 8 GPA 2.8 3.4 3.0 3.5 3.6 3.0 2.7 3.7 ŷ₁ =B₁ + B₁x₁ ACT 21 24 26 27 29 25 25 30 y X ŷ₁
The following table shows the number of hours of safety training at a large industrial plant for each of 6 divisions within the plant and the number of work hours lost due to accidents. X: Hours of safety training 10 19 30 50 65 80 Y: Work hours lost 80 65 68 35 10 12 a. Determine the Pearson's Coefficient of Correlation for the given data. Answer rounded to two decimal places b. Which of the following is an apporpriate comment for the correlation between X and Y? There is a strong positive correlation between X and Y There is a moderate positive correlation between X and Y There is a weak positive correlation between X and Y There is a strong negative correlation between X and Y There is a moderate negative correlation between X and Y
Find the new data point (x,y) in which x=2 from the data points (1.3) and (4.12)
I just need the d section
There is a negative correlation between the average litres of wine consumed by residents of a country and the incidence of national coronary deaths. Explain why correlation in this case does not imply causation by suggesting a third variable z that explains the correlation. Is z a common response or a confounding variable? Draw a digraph illustrating the between x, y, and z