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- What is regression analysis? Describe the process of performing regression analysis on a graphing utility.Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.(BIOSTATISTICS) In this question , What characteristics are associated with BMI? Use simple and multivariable linear regression analysis to complete the following table relating the characteristics listed to BMI as a continuous variable. Before conducting the analysis, be sure that all participants have complete data on all analysis variables. If participants are excluded due to missing data, the numbers excluded should be reported. Then, describe how each characteristic is related to BMI. Are crude and multivariable effects similar? What might explain or account for any differences? Outcome Variable: BMI, kg/m2 Characteristic Regression Coefficient Crude Models p-value Regression Coefficient Multivariable Model P-value Age, years 0.0627 <0.001-0.02155 0.004 Male sex -0.580 <0.001-09884 <0.001 Systolic blood pressure, mmHg 0.0603 <0.0010.05716 <0.001 Total serum cholesterol, mg/dL 0.0113 <0.0010.00638…
- The linear regression results below are based on commercial properties that real estate agencies use to guide clients with quantitative information helpful to make rental decisions. A random sample of 27 properties are considered with data for the variables, age of the property (X1), operating expenses and taxes (X2), vacancy rates (X3), total square footage (X4), and rental rates (Y). Parameter Estimates Coefficients Standard Error Intercept AGE(X1) EXPENSE(X2) VACRATE(X3) SOFOOT(X4) 14.33223914 1.457614894 -0.115608327 0.031632885 0.100364547 0.14274746 0.631648649 2.043860082 0.063765767 0.024929829 What is the upper limit of the 95% confidence interval estimate for the effect of a square footage increase on the mean rental rates of properties?We wish to determine a regression equation that relates the hourly wage in pesos to age (in years), gender and education (in years). Below is portion of the regression analysis derived using a software. *Note: under the gender variable: male and female categories are assigned a value of 0 and 1, respectively. Degrees of freedom Coefficients Standard Error Regression 5270141 3 Intercept -313.958 102.3523 Residual 6956380 121 age 8.527 1.8308 Total 12226521 124 education 46.3419 6.4502 gender -98.0447 43.1113 a) Provide the F-statistics b) Provide the F-critical value c) Are the variables jointly statistically significant? Justify your answer.We wish to determine a regression equation that relates the hourly wage in pesos to age (in years), gender and education (in years). Below is portion of the regression analysis derived using a software. *Note: under the gender variable: male and female categories are assigned a value of 0 and 1, respectively. Coefficients Standard Error SS Degrees of freedom Intercept -313.958 102.3523 Regression 5270141 3 age 8.527 1.8308 Residual 6956380 121 education 46.3419 6.4502 Total 12226521 124 gender -98.0447 43.1113 a) Interpret the age, education and gender slope coefficients b) Are age and gender coefficients statistically significant at 92% Confidence Level, justify your answer
- Please help me to write the interpretation for ANOVA and linear regression for both countries in Austria and United Kingdom (Attachment is there)Regression methods were used to analyze the data from a study investigating the relationship between roadway surface temperature (x) and pavement deflection ( y). Summary quantities were n = 20, Ey; = 12.75, Ey} = 8.86, Ex; 1478, Ex = 143,215.8, and Ex,y; = 1083.67. Calculate the least squares estimates of the slope and intercept. (Round your answer to 3 decimal places) а. 0.329 O b. 0.004 0.132 O d. 0.330true or false. Explain Fixed effects regressions remove time-varing unobservables. In an explanatory variable, precision always increases when compared to OLS.
