Ans:
Option c is correct.
The model fits the data well.
R^2 indicates the percentage of variablility in the dependent variable explained by the independent variable.
If R^2 is hight,it means that the model fits the data well.
Need Asap If R2 =.83 for a set of data and a predicted model, then: a....
Predicted concentrations of atmospheric carbon dioxide (CO2) in parts per million (ppm) are shown in the table below. (These concentrations assume that current trends continue.) 2000 2050 2100 2150 2200 CO (ppm) 364 467 600 769 987 Year a) Use the graphing calculator to make a scatterplot of the data. Let x represent years after 2000. Does the data follow a linear trend? Explain. b) The graphing calculator allows you to obtain different regression models for the given data (Stat>...
For a simple linear regression model, significance of regression is: Group of answer choices the variability of the observed Y-values from the predicted values. a hypothesis test of whether the regression coefficient ß1 is zero. a measure of how well the regression line fits the data. a measure that determines if the linearity assumption is satisfied
Construct a scatterplot and identify the mathematical model that best fits the data. Assume that the model is to be used only for the scope of the given data and consider only linear, quadratic, logarithmic, exponential, and power models. Use a calculator or computer to obtain the regression equation of the model that best fits the data. You may need to fit several models and compare the values of R2. Group of answer choices y = 2.96 x1.628 y =...
Can someone help me with these multiple choice questions. Please explain why the answers. 1. A transformation can be used to: (a) account for curvature (b) get a better prediction equation (c) stabilize the variances (d) any of the above 2. A partial regression plot that shows a straight-line relationship indicates: (a) that a linear term needs to be added to the model (b) that a quadratic term needs to be added to the model (c) the linear relationship that...
need answer asap please shows? 3. Using data on the appraised value of homes, a real estate agent computes the least- squares regression line for predicting a home's value in 2002 from its value in 1992. The equation of the least-squares regression line is: where y represents a home's value in 2002 and x is the value in 1992. $44,000+1.8x y Question: Explain y, $22,000, 1.6 and x separately? Suppose Julia owns a home that was worth $100,000 in 1992....
8. If the data set shown below were used to fit the following simple regression model, y = Bo + Bix te, which of the following equations would result in the smallest SSE? a) y=2x V X b) y=2 + x 3 2 c) y = 3 + 0.5 4 2 d) All of the above 52
You need to determine the y-intercept of the regression line for a data set. The sums of the variables and the slope are given below. Find the y-intercept, only. round to the thousandths place
A statistical model is developed by training the machine learning algorithm using training data. In most cases, this is just a subset of all the possible data for the problem for which the model is being developed. We want to develop a model that also works well with unseen data, called test data. The models that we build can overfit or underfit the data. With this in mind, which of the following statements is false: Using Ridge regression versus linear...
The coefficient of determination R2 in a simple regression model, Group of answer choices a) measures the proportion of variation in the response variable that is explained by the predictor variable b) determines the predicted value of the response variable given a value for the predictor variable c) estimates the difference between averages in the response variable when the predictor variable differs by 1 d) indicates the predictive ability of the model
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2011.4 log()0.02 r2 0.06 Model D: Model E: y = 5.4 + 0.82i --3.4 55.1 log(0.020 2 + 1.2r2 0.2 (1x2) Ceteris Paribus: (a) In Model A: If x1 increases 6 to 8 by 2 units, then the predicted change in y is Δy =...