2)and a new HPLC instrument. Compare the 2 data sets using graphed regression analysis in Excel tosee if the methods can be determined as equal. Discuss your conclusion. You should provide ascreengrab of your graph(s).The level of phthalates in 20 water samples was determined by an old HPLC instrumentOld HPLCNew HPLC110.110.5211.311.0312.411.449.89.358.68.39.28.9710.210.3810.610.38.99.31010.810.91111.511.01212.412.11310.210.8149.89.9158.47.9168.48.01710.410.9189.810.5199.310.62011.211.1

S.L. No. Old HPLC New HDLC 1 10.1 10.5 2 11.3 11 3 12.4 11.4 4 9.8 9.3 5 8.6 8.3 6 9.2…

Answered: 2) and a new HPLC instrument. Compare…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 6SGR

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The coefficient of determination of a set of data points is 0.98 and the slope of the regression line is -4.28. Determine the linear correlation coefficient of the data.
A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce the following regression output that relates the city gas mileage (in mpg) and the engine displacement (in cubic inches). The regression equation is mpg_city= 35.5 - 0.0696 * displacement We have a car that has an engine with 141 cubic inches. Based on this output, what city gas mileage would you predict for this car?____ (Round answer to the nearest hundredth (2 decimal places.)
Use the Manufacturing database from “Excel Databases.xls” on Blackboard. Use Excel to develop a multiple regression model to predict Cost of Materials by Number of Employees, New Capital Expenditures, Value Added by Manufacture, and End-of-Year Inventories. Use Excel to perform a test of the overall model. Write the test statistic. Round your answer to 2 decimal places SIC Code No. Emp. No. Prod. Wkrs. Value Added by Mfg. Cost of Materials Value of Indus. Shipmnts New Cap. Exp. End Yr. Inven. Indus. Grp. 201 433 370 23518 78713 4 1833 3630 1 202 131 83 15724 42774 4 1056 3157 1 203 204 169 24506 27222 4 1405 8732 1 204 100 70 21667 37040 4 1912 3407 1 205 220 137 20712 12030 4 1006 1155 1 206 89 69 12640 13674 3 873 3613 1 207 26 18 4258 19130 3 487 1946 1 208 143 72 35210 33521 4 2011 7199 1 209 171 126 20548 19612 4 1135 3135 1 211 21 15 23442 5557 3 605 5506 2 212 3 2 287 163 1 2 42 2 213 2 2 1508 314 1 15 155 2 214 6 4 624 2622 1 27 554 2 221…
define and state the difference between the three terms association, correlation and causation. find the regression line between the height of the strawberry plant and the water it gets per day and interpret the intercept and slope coefficients. find a 95% confidence interval around the plant estimate of the slope parameter from your model.
four pairs of data yield r = 0.942 and the regression equation y-cap = 3x. also, y-bar= 12.75. what is the best predicted value of y for x = 4.6?
Using the California Department of Education's API 2000 dataset with sample size be 45. This data file contains a measure of school academic performance as well as other attributes of the elementary schools, such as, class size, enrollment, poverty, etc. Let's dive right in and perform a regression analysis using the variables api00, acs_k3, meals and full. Fit the regression model and get the following SAS output. The following are the outputs from SAS. Source Model Error Corrected Total Variable Intercept acs_k3 meals full Label DF 3 a b Intercept avg class size k-3 pct free meals. pct full credential Sum of Squares DF Analysis of Variance 1 1 1 1 с 1271 e Mean Square 875 d Parameter Estimates Parameter Estimate 875.05923 -2.50651 -3.31802 0.12031 Answer the question using the SAS output. F Value f Standard Error 31.03465 1.59469 0.23408 0.12072 Pr > F g t Value 32.08 -1.92 -24.04 1.20 Pr > |t| <.0001 0.0553 <.0001 0.2321
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+BOIL+yEXP+8FDI Where FR = yearly foreign reserves ($000's), OIL = annual oil prices, EXP = yearly total exports ($000's) and FDI = annual foreign direct investment ($000's). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Сoef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq 96.3% R-sq (adj) 95.3% Analysis of Variance Source DF MS F Regression 1991.31 663.77 ?? Error 12 77.4 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values *', ***', '?'and ??' d) Hence test whether B is significant. Give reasons for your answer. e) Perform the F…
A real estate agent wanted to find the relationship between sale price of houses and the size of the house. Shecollected data on two variables recorded in the following table for 15 houses in Seattle. The two variables are PRICE= Sale price of houses in thousands of dollarsSIZE= Area of the entire house in square feet. The Excel working has been given. Note: the left hand side is Regression run.. The right hand side is 'new' regression run (for ans d). Question a) Using MICROSOFT EXCEL- run the above regression and copy the output into your assignment word documentfrom which you can write down the least square regression line. Write down the least square regression linefrom that specific output. b) Interpret the slope and constant term with proper UNITS assigned. c) Comment on the explanatory power of the regression model from the required output. Copy that specificoutput into your assignment word document.Now to increase the explanatory power of the model the real estate agent decides…
Four pairs of data yield r= 0.942 and regression equation y=3x.Also, y= 12.75. What is the best predicted value of y for x= 2.9?
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is1.8cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead_Width_(cm) Weight_(kg)7.2 1287.4 1679.8 2619.5 2218.7 2118.3 201 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) Part 2 The best predicted weight for an overhead width of 1.8 cm, based on the regression equation, is enter your response here kg. (Round to one decimal place as needed.) Part 3 Can the prediction be correct? If not, what is wrong? A. The prediction cannot be correct…
A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 132 7.4 170 9.8 268 9.4 224 8.9 225 8.4 209 Q The regression equation is y=-162+ (43.1)x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) The best predicted weight for an overhead width of 2.1 cm, based on the regression equation, is -71.5 kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? OA. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample…

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