What are finite differences, and how are they used to estimating derivatives of gridded data
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- What are finite differences, and how are they used to estimating derivatives of gridded data?
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- What is regression analysis? Describe the process of performing regression analysis on a graphing utility.27) Below is some of the regression output from a regression of the price of a copier (expressed in $'s) based on the speed (pages per minute) and whether it copies in color or not (color=0 if it does NOT copy in color and =1 if it does copy in color) A copier manufacturer is trying to decide how much to charge for their new copier. Based on the regression output, if a printer can print 4.8 pages per minute and copies in color, what is the predicted price? *please round to 2 decimal places*The average August temperatures (y) and geographic latitudes (x) of 13 cities in the United States were studied. The regression equation for these data isTemperature = 127 -1.68*(latitude)What is the slope of the line? Estimate the mean August temperature for a city with latitude of 11.Use 2 decimal placesSan Francisco has a latitude of 40. What would you predict for the mean August temperature of San Francisco?Use 2 decimal placesGiven that the mean August temperature in San Francisco is actually 70 calculate the residual (prediction error) for San Francisco.It should be observed minus predicted. If you do it wrong your answer have the positive and negative switchedUse 2 decimal placesThe latitude at the equator is 0. Estimate the average August temperature at the equator.Use 1 decimal place
- Explain how to choose the dependent and independent variables in regression analysis used for costestimation(a) If per capita GDP rises by $1,000, by how much does the % of GDP spent on health change? (b) If the slope coefficient equals zero what is the implied income elasticity of health ex- penditures? Explain. (c) Test at 5% whether or not there is a relationship between healthgdp and gdppc.discuss how the coefficient of determination and the coefficient of correlation are related and how you would use them in regression analysis
- Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative?Finally , the researcher considers using regression analysis to establish a linear relationship between the two variables – hours worked per week and yearly income. a) What is the dependent variable and independent variable for this analysis? Why? b) Use an appropriate plot to investigate the relationship between the two variables. Display the plot. On the same plot, fit a linear trend line including the equation and the coefficient of determination R2. Data Hours Per Week Yearly Income ('000's) Class 18 43.8 13 44.5 18 44.8 25.5 46.0 11.5 41.2 18 43.3 16 43.6 27 46.2 27.5 46.8 30.5 48.2 24.5 49.3 32.5 53.8 25 53.9 23.5 54.2 30.5 50.5 27.5 51.2 28 51.5 26 52.6 25.5 52.8 26.5 52.9 33 49.5 15 49.8 27.5 50.3 36 54.3 27 55.1 34.5 55.3 39 61.7 37 62.3 31.5 63.4 37 63.7 24.5 55.5 28 55.6 19 55.7 38.5 58.2 37.5 58.3 18.5 58.4 32 59.2 35 59.3…Please help with no. 4! :/
- Why this type of plot is used in analysing regression modelsA multiple regression model has the form y^=b0+b1x1+b2x2 The coefficient b1 is interpreted as the:A. change in y per unit change in x1, holding x2 constantB. change in the average value of y per unit change in x1, holding x2 constantC. change in y per unit change in x1, when x1 and x2 values are correlatedD. change in y per unit change in x1 If multicollinearity exists among the independent variables included in a multiple regression model, the:A. multiple coefficient of determination will assume a value close to zeroB. standard errors of the regression coefficients for the correlated independent variables will increaseC. regression coefficients will be difficult to interpretD. regression coefficients will be difficult to interpret and the standard errors of the regression coefficients for the correlated independent variables will increase please explain which answer is correct and whyA researcher is analyzing the relationship between various variables in housing data for 32 cities: median list prices of single family homes, condominium or co-ops, all homes, median household income, unemployment rate, and population. What is the regression equation for the model that predicts the list price of all homes using unemployment rate as an explanatory variable? Y^=____________X+(_____________) What is the interpretation of the slope? (INCREASE OR DECREASE) in the list price of all homes as unemployment rate increases. What is the interpretation of the intercept? (LIST PRICE OF ALL HOMES WHEN UNEMPLOYMENT RATE IS ZERO, NO PRATICAL INTERCEPTION, UNEMPLOYMENT RATE WHEN LIST PRICE OF ALL HOMES IS ZERO )