ht then click on format trendline.4.Click on linear and choose line of equation and R2 val ue.5.Then ok.Solution:Here we have to find the equation of line and graph.So we get ap propriate graph and the equation of line using excel.Steps: 1.Enter x and y values in exce I sheet.2.Select x and y values and click on insert then click on scatter plot.3.Click on any point on the graph click right then click on format trendline.4.Click on linear and choose line of equation and R2 value.5.Then ok. NO 40 94 20 10 0 Scatter plot -3.7791x 4476? ²-0,9709 . 12 Serial Linear Berest) 34 So we get the equation of line is,y=-4.4767+3.7791xAnd we get R2=0.9709So we get th e equation of line is,y=-4.4767+3.7791xAnd we get R2=0.9709 Now we have to predict y at x=15.y=-4.4767+3.7791xput x=15 in the above equationy= -4.4767+3.7791(15)y=52.2098so we predict y at x=15 isy=52Now we have to predict y a tx=15.y=-4.4767+3.7791xput x=15 in the above equationy=- 4.4767+3.7791(15)y=52.2098so we predict y at x=15 isy=52 Process of fitting of linear regression model. The linear regression model is,y=mx+c Whe re, Independent variable (x) and dependent variable (y).m is the slope and c is constant. It helps us to estimate the contribution of independent variable/variables (X or group of Xs) on the dependent variable (Y).In fitting we estimate the parameter m and c.For che cking our model is good fit or not we find R2 value.R2: The most common interpretation of r-squared is how well the regression model explains observed data. In our case R2= 97% reveals that 97% of the variability observed in the target variable is explained by th e regression model. So we can say that our model is good fit.From the R2 value we can s ay that the linear model is the most appropriate model to the given x and y valueswhich is good fit. 5) Based on your equation if you have 15 x's, how many y's would your model predict? 6) Think about the overall process required to build a good model. Describe the process.
ht then click on format trendline.4.Click on linear and choose line of equation and R2 val ue.5.Then ok.Solution:Here we have to find the equation of line and graph.So we get ap propriate graph and the equation of line using excel.Steps: 1.Enter x and y values in exce I sheet.2.Select x and y values and click on insert then click on scatter plot.3.Click on any point on the graph click right then click on format trendline.4.Click on linear and choose line of equation and R2 value.5.Then ok. NO 40 94 20 10 0 Scatter plot -3.7791x 4476? ²-0,9709 . 12 Serial Linear Berest) 34 So we get the equation of line is,y=-4.4767+3.7791xAnd we get R2=0.9709So we get th e equation of line is,y=-4.4767+3.7791xAnd we get R2=0.9709 Now we have to predict y at x=15.y=-4.4767+3.7791xput x=15 in the above equationy= -4.4767+3.7791(15)y=52.2098so we predict y at x=15 isy=52Now we have to predict y a tx=15.y=-4.4767+3.7791xput x=15 in the above equationy=- 4.4767+3.7791(15)y=52.2098so we predict y at x=15 isy=52 Process of fitting of linear regression model. The linear regression model is,y=mx+c Whe re, Independent variable (x) and dependent variable (y).m is the slope and c is constant. It helps us to estimate the contribution of independent variable/variables (X or group of Xs) on the dependent variable (Y).In fitting we estimate the parameter m and c.For che cking our model is good fit or not we find R2 value.R2: The most common interpretation of r-squared is how well the regression model explains observed data. In our case R2= 97% reveals that 97% of the variability observed in the target variable is explained by th e regression model. So we can say that our model is good fit.From the R2 value we can s ay that the linear model is the most appropriate model to the given x and y valueswhich is good fit. 5) Based on your equation if you have 15 x's, how many y's would your model predict? 6) Think about the overall process required to build a good model. Describe the process.
Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
1st Edition
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:HOUGHTON MIFFLIN HARCOURT
Chapter10: Radical Functions And Equations
Section: Chapter Questions
Problem 2CA
Related questions
Question
Transcribed Image Text:ht then click on format trendline.4.Click on linear and choose line of equation and R2 val
ue.5.Then ok.Solution:Here we have to find the equation of line and graph.So we get ap
propriate graph and the equation of line using excel.Steps: 1.Enter x and y values in exce
I sheet.2.Select x and y values and click on insert then click on scatter plot.3.Click on any
point on the graph click right then click on format trendline.4. Click on linear and choose
line of equation and R2 value.5.Then ok.
50
50
> 30
20
10
0
O
Scatter plot
y-3.7791x-4.4767
R*-0.9709
-Linear (Series1)
14
So we get the equation of line is,y=-4.4767+3.7791xAnd we get R2=0.9709So we get th
e equation of line is,y=-4.4767+3.7791xAnd we get R2=0.9709
Now we have to predict y at x=15.y=-4.4767+3.7791xput x=15 in the above equationy=
-4.4767+3.7791(15)y=52.2098so we predict y at x=15 isy=52Now we have to predict y a
tx=15.y=-4.4767+3.7791xput x-15 in the above equationy=-
4.4767+3.7791(15)y=52.2098so we predict y at x=15 isy=52
Process of fitting of linear regression model. The linear regression model is,y=mx+c Whe
re, Independent variable (x) and dependent variable (y).m is the slope and c is constant.
It helps us to estimate the contribution of independent variable/variables (X or group of
Xs) on the dependent variable (Y).In fitting we estimate the parameter m and c.For che
cking our model is good fit or not we find R2 value.R2: The most common interpretation
of r-squared is how well the regression model explains observed data. In our case R2=
97% reveals that 97% of the variability observed in the target variable is explained by th
e regression model.So we can say that our model is good fit. From the R2 value we can s
ay that the linear model is the most appropriate model to the given x and y valueswhich
is good fit.
5) Based on your equation if you have 15 x's, how many y's would your model
predict?
6) Think about the overall process required to build a good model. Describe the
process.
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