Above are the set of questions i need to be answered. Use the data set below tha
ID: 3320942 • Letter: A
Question
Above are the set of questions i need to be answered. Use the data set below that i have provided to you to answer the following questions in the first picture. I had to seperate the data set into a couple of different pics because it was too long for just one pic. Just follow the pics down and the data table should make sense.
Use the data provided to describe the relationship between weight measures. Feel free to use the analysis features of Excel to calculate the descriptive terms. Plot a frequency histogram for hatch and 2 week weight. Use 5 and 50 gram increments respectively. Mean, variance, stdev, stderr for Hatch weight. Mean, variance, stdev, stderr for 2 week body weight. Covariance, correlation, and regression for hatch and 2 week weight Plot a graph descriptive of the calculated regression equation Predict the 2 week weight for a chick that weighs 38 and 50 grams respectively. Using the same data set use the Z-score method to select the top 15 percent of the birds for increased 2-week body weight (chapter 11). What would be the recommended cut off weight? Using the same z-score method, what proportion of the population falls between 300 and 500 grams for 2-week body weight?Explanation / Answer
Since the dataset provided is too huge to be typed in manually in excel on the basis of pics at my end, I will discuss a more general methodology that you can use at your end to get all the variables you need.
An open source software like R will be highly useful, you can read in all of this data as a dataframe in using the read.csv() function in R and then do the following analysis
a. You can use the function hist() in R to directly plot the histogram of hatch and two week weights in R. Just pass the column names one by one into this function as input and exceute onthe console. Frequency histograms will be directly plotted.
b.You can use the functions like mean(), var() and sd() and std.error() in R and just pass in the Hatch wt. column name as input to get the required values of mean variance and std. deviation and standard error
c. Again, You can use the functions like mean(), var() and sd() and std.error() in R and just pass in the 2 wk body wt. column name as input to get the required values of mean variance and std. deviation and standard error
d . You can use the functions like cov(), cor() and lm() in R and just pass in the column names for which you want to calculate the covariance and correlation for. The lm() function stands for linear model and will give you a complete summary of the intercepts, coeff and R^2 values for the two columns you are performing the regression on.
Hence all of the above parameters can be obtained easily and in just one step using R. It is advised that you try this at your end.