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If r is not significant, should a regression be done?
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No it should not.
There is not a
significant linear relationship between x and y. Therefore we can NOT
use the regression line to model a linear relationship between x and y
in the population.
Please let me know if you need any clarification. I'm always happy to answer your questions.
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HLTH 501 Penn State Technology and Suicide Among School Age Youth Discussion
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HLTH 501 Penn State Technology and Suicide Among School Age Youth Discussion
Describe the study sample.Compare risk factors in men and women.What characteristics are associated with BMI?Who is most likely to have prevalent coronary heart disease?Describe the Data collection Methods used in the studyPlease Provide a Summary of the data analysis as a whole . Summary should include pertinent information of all the questions above. This summary should not exceed 300 words. 1. Complete the following table to describe the study sample using data collected at the first examination for each participant (n = 4434). Summarize your results in three to four sentences. Patient Characteristic* Total Sample (n = 4434) Age, years 49.93 (8.68) Male sex 1944 (43.84) Systolic blood pressure, mmHg 132.91 (22.42) Diastolic blood pressure, mmHg 83.08 (12.06) Use of anti-hypertensive medication 144 (3.29) Current smoker 1944 (43.84) Total serum cholesterol, mg/dL 236.98 (44.65) Body mass index 25.85 (4.10) Diabetes 121 (2.73) * Mean (Standard deviation) or n (%) 2. Complete the following table to compare men and women using data collected at the first examination for each participant (n = 4434). Summarize your results in three to four sentences. Patient Characteristic* Men (n = 1944) Women (n = 2490) Age, years 49.79 (8.72) 50.03 (8.64) Systolic blood pressure, mmHg 131.74 (19.44) 133.82 (24.46) Diastolic blood pressure, mmHg 83.71 (11.44) 82.60 (12.50) Use of anti-hypertensive medication 42 (2.19) 102 (4.16) Current smoker 1175 (60.44) 1006 (40.40) Total serum cholesterol, mg/dL 233.58 (42.36) 239.68 (46.22) Body mass index 26.17 (3.41) 25.60 (4.56) Diabetes 59 (3.04) 62 (2.49) * Mean (Standard deviation) or n (%) 3.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 -0.9884 <0.001 Systolic blood pressure, mmHg 0.0603 <0.001 0.05716 <0.001 Total serum cholesterol, mg/dL 0.0113 <0.001 0.00638 <0.001 Current smoker -1.4017 <0.001 -1.2818 <0.001 Diabetes 2.2918 <0.001 1.2355 0.001 4. Test if there are significant differences in the following risk factors between persons with and without prevalent coronary heart disease (CHD). Summarize the statistical results in the table below and then compare risk factors in persons with and without prevalent CHD. Be sure to indicate what statistical tests were used in the footnote to the table and in a brief summary of a paragraph or less. Patient Characteristic* History of CHD (n = 194) No History of CHD (n = 4240) p-value* Age, years 57.48 (7.42) 49.58 (8.57) <0.001 Systolic blood pressure, mmHg 144.99 (27.03 132.35 (22.03) <0.001 Diastolic blood pressure, mmHg 87.14 (14.33) 82.90 (11.91) <0.001 Total serum cholesterol, mg/dL 243.19 (45.61) 236.69 (44.6) 0.0486 Body mass index 26.83 (4.45) 25.80 (4.08) <0.001 * Mean (Standard deviation). P-values are based on two independent samples t tests.5. Describe the Data collection Methods used in the study6. Please Provide a Summary of the data analysis as a whole . Summary should include pertinent information of all the questions above. This summary should not exceed 300 words.
MAT 240 SNHU Relationship BW Selling Price of Properties and Their Sizes Analysis
ScenarioSmart businesses in all industries use data to provide an intuitive
analysis of how they can ge ...
MAT 240 SNHU Relationship BW Selling Price of Properties and Their Sizes Analysis
ScenarioSmart businesses in all industries use data to provide an intuitive
analysis of how they can get a competitive advantage. The real estate
industry heavily uses linear regression to estimate home prices, as cost
of housing is currently the largest expense for most families.
Additionally, in order to help new homeowners and home sellers with
important decisions, real estate professionals need to go beyond showing
property inventory. They need to be well versed in the relationship
between price, square footage, build year, location, and so many other
factors that can help predict the business environment and provide the
best advice to their clients.PromptYou have been recently hired as a junior analyst by D.M. Pan Real
Estate Company. The sales team has tasked you with preparing a report
that examines the relationship between the selling price of properties
and their size in square feet. You have been provided with a Real Estate County Data
document that includes properties sold nationwide in recent years. The
team has asked you to select a region, complete an initial analysis, and
provide the report to the team.Note: In the report you prepare for the sales team,
the response variable (y) should be the median listing price and the
predictor variable (x) should be the median square feet.Specifically you must address the following rubric criteria, using the Module Two Assignment Template:Generate a Representative Sample of the Data
Select a region and generate a simple random sample of 30 from the data.Report the median listing price and median square foot, report the mean, median, and standard deviation.
Analyze Your Sample
Discuss how the regional sample created is or is not reflective of the national market.
Compare and contrast your sample with the population using the National Statistics and Graphs document.
Explain how you have made sure that the sample is random.
Explain your methods to get a truly random sample.
Generate Scatterplot
Create a scatterplot of the x and y variables noted above and include a trend line and the regression equation
Observe patterns
Answer the following questions based on the scatterplot:
Define x and y. Which variable is useful for making predictions?Is there an association between x and y? Describe the association you see in the scatter plot.What do you see as the shape (linear or nonlinear)?If you had a 1,200 square foot house, based on the regression equation in the graph, what price would you choose to list at?Do you see any potential outliers in the scatterplot?
Why do you think the outliers appeared in the scatterplot you generated?What do they represent?
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Most Popular Content
HLTH 501 Penn State Technology and Suicide Among School Age Youth Discussion
Describe the study sample.Compare risk factors in men and women.What characteristics are associated with BMI?Who is most ...
HLTH 501 Penn State Technology and Suicide Among School Age Youth Discussion
Describe the study sample.Compare risk factors in men and women.What characteristics are associated with BMI?Who is most likely to have prevalent coronary heart disease?Describe the Data collection Methods used in the studyPlease Provide a Summary of the data analysis as a whole . Summary should include pertinent information of all the questions above. This summary should not exceed 300 words. 1. Complete the following table to describe the study sample using data collected at the first examination for each participant (n = 4434). Summarize your results in three to four sentences. Patient Characteristic* Total Sample (n = 4434) Age, years 49.93 (8.68) Male sex 1944 (43.84) Systolic blood pressure, mmHg 132.91 (22.42) Diastolic blood pressure, mmHg 83.08 (12.06) Use of anti-hypertensive medication 144 (3.29) Current smoker 1944 (43.84) Total serum cholesterol, mg/dL 236.98 (44.65) Body mass index 25.85 (4.10) Diabetes 121 (2.73) * Mean (Standard deviation) or n (%) 2. Complete the following table to compare men and women using data collected at the first examination for each participant (n = 4434). Summarize your results in three to four sentences. Patient Characteristic* Men (n = 1944) Women (n = 2490) Age, years 49.79 (8.72) 50.03 (8.64) Systolic blood pressure, mmHg 131.74 (19.44) 133.82 (24.46) Diastolic blood pressure, mmHg 83.71 (11.44) 82.60 (12.50) Use of anti-hypertensive medication 42 (2.19) 102 (4.16) Current smoker 1175 (60.44) 1006 (40.40) Total serum cholesterol, mg/dL 233.58 (42.36) 239.68 (46.22) Body mass index 26.17 (3.41) 25.60 (4.56) Diabetes 59 (3.04) 62 (2.49) * Mean (Standard deviation) or n (%) 3.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 -0.9884 <0.001 Systolic blood pressure, mmHg 0.0603 <0.001 0.05716 <0.001 Total serum cholesterol, mg/dL 0.0113 <0.001 0.00638 <0.001 Current smoker -1.4017 <0.001 -1.2818 <0.001 Diabetes 2.2918 <0.001 1.2355 0.001 4. Test if there are significant differences in the following risk factors between persons with and without prevalent coronary heart disease (CHD). Summarize the statistical results in the table below and then compare risk factors in persons with and without prevalent CHD. Be sure to indicate what statistical tests were used in the footnote to the table and in a brief summary of a paragraph or less. Patient Characteristic* History of CHD (n = 194) No History of CHD (n = 4240) p-value* Age, years 57.48 (7.42) 49.58 (8.57) <0.001 Systolic blood pressure, mmHg 144.99 (27.03 132.35 (22.03) <0.001 Diastolic blood pressure, mmHg 87.14 (14.33) 82.90 (11.91) <0.001 Total serum cholesterol, mg/dL 243.19 (45.61) 236.69 (44.6) 0.0486 Body mass index 26.83 (4.45) 25.80 (4.08) <0.001 * Mean (Standard deviation). P-values are based on two independent samples t tests.5. Describe the Data collection Methods used in the study6. Please Provide a Summary of the data analysis as a whole . Summary should include pertinent information of all the questions above. This summary should not exceed 300 words.
MAT 240 SNHU Relationship BW Selling Price of Properties and Their Sizes Analysis
ScenarioSmart businesses in all industries use data to provide an intuitive
analysis of how they can ge ...
MAT 240 SNHU Relationship BW Selling Price of Properties and Their Sizes Analysis
ScenarioSmart businesses in all industries use data to provide an intuitive
analysis of how they can get a competitive advantage. The real estate
industry heavily uses linear regression to estimate home prices, as cost
of housing is currently the largest expense for most families.
Additionally, in order to help new homeowners and home sellers with
important decisions, real estate professionals need to go beyond showing
property inventory. They need to be well versed in the relationship
between price, square footage, build year, location, and so many other
factors that can help predict the business environment and provide the
best advice to their clients.PromptYou have been recently hired as a junior analyst by D.M. Pan Real
Estate Company. The sales team has tasked you with preparing a report
that examines the relationship between the selling price of properties
and their size in square feet. You have been provided with a Real Estate County Data
document that includes properties sold nationwide in recent years. The
team has asked you to select a region, complete an initial analysis, and
provide the report to the team.Note: In the report you prepare for the sales team,
the response variable (y) should be the median listing price and the
predictor variable (x) should be the median square feet.Specifically you must address the following rubric criteria, using the Module Two Assignment Template:Generate a Representative Sample of the Data
Select a region and generate a simple random sample of 30 from the data.Report the median listing price and median square foot, report the mean, median, and standard deviation.
Analyze Your Sample
Discuss how the regional sample created is or is not reflective of the national market.
Compare and contrast your sample with the population using the National Statistics and Graphs document.
Explain how you have made sure that the sample is random.
Explain your methods to get a truly random sample.
Generate Scatterplot
Create a scatterplot of the x and y variables noted above and include a trend line and the regression equation
Observe patterns
Answer the following questions based on the scatterplot:
Define x and y. Which variable is useful for making predictions?Is there an association between x and y? Describe the association you see in the scatter plot.What do you see as the shape (linear or nonlinear)?If you had a 1,200 square foot house, based on the regression equation in the graph, what price would you choose to list at?Do you see any potential outliers in the scatterplot?
Why do you think the outliers appeared in the scatterplot you generated?What do they represent?
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