82979_1_637456281290403355_82979

Use Excel Spreadsheets, Google Sheets, or GeoGebra to create a scatter plot for the data below. (You will upload your graph in the next question.) Use the graph to answer the questions below the table.  The following data are the morning and evening high tide levels for Charleston, SC from January 1-14,2017. The information for the PM high tide for January 4 is missing. Create a scatter plot. Find the regression line and use it to estimate the PM high tide for January 4. Then find the correlation coefficient. (NOTE: The first column identifies the day. This data will not be used in the scatter plot.)  Day AM High (in feet), xx PM High (in feet), yy 1 5.6 4.8 2 5.5 4.8 3 5.4 4.9 4 5.2   5 5.0 5.1 6 5.2 5.0 7 5.4 4.9 8 5.7 5.0 9 6.0 5.1 10 6.3 5.3 11 6.4 5.4 12 6.5 5.4 13 6.4 5.4 14 6.2 5.3 Source: SCDHEC.gov

Calculate the regression coefficient and obtain the lines of regression for the following data: USE MICROSOFT EXCEL

Create a scatterplot of the data. Choose the correct graph Identify a characteristic of the data that is ignored by the regression line.

Interpret the values of the regression coefficients (intercept and slope)in this context. Note whether the intercept is meaningful (valid) for this data set.

Draw the weights and heights scatter diagram of your data by using EXCEL and give your interpretation.   Find the equation of the regression line from the following data

Open the Excel spreadsheet "HomeSalesData.xls". This dataset shows actual home sales data from New Orleans, circa 2005. Estimate a regression to answer the following. Each square foot increase in home size is associated with a _____________ dollar increase home price. Round your answer to two decimal places.  Data on Potentially Related Variables for Randomly Selected Homes           Price Home_Size Lot_Size Number_Rooms Number_Baths $72,000 600 0.50 3 1.0 $116,300 1050 0.43 5 1.5 $152,000 1800 0.68 7 1.5 $80,500 922 0.30 5 1.0 $141,900 1950 0.75 8 2.5 $124,000 1783 0.22 8 1.5 $117,000 1008 0.50 6 1.0 $165,900 1840 1.16 8 2.0 $153,500 3700 1.10 10 3.0 $126,500 1092 0.26 6 1.0 $122,000 1950 0.50 7 1.5 $140,000 1403 0.50 6 2.0 $223,000 1680 14.37 8 2.0 $99,500 1000 0.49 4 1.0 $211,900 2310 0.46 8 2.5 $121,900 1300 0.78 6 1.0 $169,000 1930 3.00 9 3.0 $156,000 3000 0.50 11 2.5 $123,500 1362 0.40 7 2.0 $136,000 1750 0.50 7 2.0 $194,900 2080…

Find the regression model for the data given

which histogram correctly shows the data from the table

Open the Excel spreadsheet "HomeSalesData.xls". This dataset shows actual home sales data from New Orleans, circa 2005. Estimate a regression to answer the following. Each additional BATH in a house is associated with a _____________ dollar increase home price. Round your answer to two decimal places.  Price Home_Size Lot_Size Number_Rooms Number_Baths $72,000 600 0.50 3 1.0 $116,300 1050 0.43 5 1.5 $152,000 1800 0.68 7 1.5 $80,500 922 0.30 5 1.0 $141,900 1950 0.75 8 2.5 $124,000 1783 0.22 8 1.5 $117,000 1008 0.50 6 1.0 $165,900 1840 1.16 8 2.0 $153,500 3700 1.10 10 3.0 $126,500 1092 0.26 6 1.0 $122,000 1950 0.50 7 1.5 $140,000 1403 0.50 6 2.0 $223,000 1680 14.37 8 2.0 $99,500 1000 0.49 4 1.0 $211,900 2310 0.46 8 2.5 $121,900 1300 0.78 6 1.0 $169,000 1930 3.00 9 3.0 $156,000 3000 0.50 11 2.5 $123,500 1362 0.40 7 2.0 $136,000 1750 0.50 7 2.0 $194,900 2080 1.00 8 2.5 $128,500 1344 0.94 6 2.0 $302,000 2130 11.91 8 1.5 $142,000 1500 0.41 7…

Interpret the regression results.

Determine the type of variation model that best fits the data in the attached image.

The most popular colors for compact and sports cars in a recent year aregiven in the table. See Attachment Use an appropriate graphical display to describe these data.

Find the mean of the regression squares

Spring is a peak time for selling houses. The file SpringHouses contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018 (realtor.com website) Click on the datafile logo to reference the data. DATA file a. The Excel output for the estimated regression equation that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house: SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression statistics Regression Residual Total Multiple R R Square ANOVA Adjusted R Square Standard Error Observations 0.7429 0.5519 0.4907 61948.6931 Regression statistics Regression Residual Total df Intercept Sq Ft Beds Lower 95% 0.9353 -145129.5298 Intercept Baths 0.9528 -49383.5243 0.0180 Sq Ft Beds 0.0326 Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R is greater than 45%, the…

MLR model, using r dataset, state.x77, with Population as the dependent variable, and Income, Life.Exp, HS.Grad, and Frost as the independent variables

In the picture, there is a summary of regression analysis.  Interpret the results in terms of overall model fit .  Interpret the results in terms of the meaning of each estimated coefficient.

Download/open DJIAS_P500.xlsx and run a regression report using DJIA to predict S&P 500. In the blank below, report the value of the regression coefficient that I corresponds to the slope intercept, bo. Round your answer to two (2) decimal places if necessary. Your Answer:

Determine the quadratic regression for the data set below. (2,762),(5,939),(7,1177),(10,1714),(12,2192),(15,3089)

Solve using excel or spreadsheet and show data analysis (regression table)

independently analyze and interpret the data. Compare the improved airplane performance (average flight time) with the baseline results determined.