Select any two quantitative variables correlated to each other’s, such as corn yields or other commodity data, https://www.nass.usda.gov/Quick_Stats

Select any two quantitative variables correlated to each other’s, such as corn yields or other commodity data, https://www.nass.usda.gov/Quick_Stats/(dependent or response variable) from the USDA site and rainfall data (independent or classification variable). Other examples could be the gasoline price at the pump in the USA, crude oil production in 2020, or current obesity (BMI) levels and education levels in the US population (www.cdc.gov). Collect at least 20 data points on each of the two selected variables. This exercise is vital to understanding and collecting secondary data from different sources. Alternatively, you can use the attached Framingham Heart Study Data. Select any two quantitative variables from this data set (that make logical sense), perform correlation analysis, and develop your manuscript

Once you collect the required data, please place it in two separate columns in Excel and then perform correlation and regression analyses; the example spreadsheet is given along with the data set. Besides reading chapters 3 & 4 contents and slides, carefully read the Model content as it explains in a step-by-step manner how to calculate ‘r”, the correlation coefficient, and regression coefficients.Select any two quantitative variables correlated to each other’s, such as corn yields or other commodity data, https://www.nass.usda.gov/Quick_Stats/(dependent or response variable) from the USDA site and rainfall data (independent or classification variable). Other examples could be the gasoline price at the pump in the USA, crude oil production in 2020, or current obesity (BMI) levels and education levels in the US population (www.cdc.gov). Collect at least 20 data points on each of the two selected variables. This exercise is vital to understanding and collecting secondary data from different sources. Alternatively, you can use the attached Framingham Heart Study Data. Select any two quantitative variables from this data set (that make logical sense), perform correlation analysis, and develop your manuscript

Once you collect the required data, please place it in two separate columns in Excel and then perform correlation and regression analyses; the example spreadsheet is given along with the data set. Besides reading chapters 3 & 4 contents and slides, carefully read the Model content as it explains in a step-by-step manner how to calculate ‘r”, the correlation coefficient, and regression coefficients.Select any two quantitative variables correlated to each other’s, such as corn yields or other commodity data, https://www.nass.usda.gov/Quick_Stats/(dependent or response variable) from the USDA site and rainfall data (independent or classification variable). Other examples could be the gasoline price at the pump in the USA, crude oil production in 2020, or current obesity (BMI) levels and education levels in the US population (www.cdc.gov). Collect at least 20 data points on each of the two selected variables. This exercise is vital to understanding and collecting secondary data from different sources. Alternatively, you can use the attached Framingham Heart Study Data. Select any two quantitative variables from this data set (that make logical sense), perform correlation analysis, and develop your manuscript

Once you collect the required data, please place it in two separate columns in Excel and then perform correlation and regression analyses; the example spreadsheet is given along with the data set. Besides reading chapters 3 & 4 contents and slides, carefully read the Model content as it explains in a step-by-step manner how to calculate ‘r”, the correlation coefficient, and regression coefficients.