MATH 325 DeVry Week 2 iLab

admin   July 1, 2017   Comments Off on MATH 325 DeVry Week 2 iLab

MATH 325 DeVry Week 2 iLab

Downloading is very simple, you can download this Course here:


Contact us at:


MATH 325 DeVry Week 2 iLab


MATH 325 DeVry Week 2 iLab

MATH 325 DeVry Week 2 iLab

Submit the output file as your Week 2 iLab.

Context! (Remember that statistics are far more than numbers or values – you need to know the context to perform a good analysis.) Asthmatics are commonly admitted to the hospital to help manage their asthma. They may require just a short stay, but it is not unusual that a long stay may be required in cases that also involve pneumonia or other factors. The hospital patient satisfaction survey given to these patients is reporting the patient’s overall assessment, and these values can range from 0 to 10.

Measures of Central Tendency and Variability

The steps required for completing the deliverables for this assignment (screen shots that correspond to these instructions can be found immediately following them).Complete the questions below and paste the answers from Minitab below each question (type your answers to the questions where noted). Therefore, your response to the lab will be this ONE document submitted to the Dropbox.

  1. Open Minitab
  2. Open the Health Care Data .mpjfile using Minitab.
  3. From Menus, select Stat, Basic Statistics and then Display Descriptive Statistics
    1. Choose Hosp, StayandHosp, Satisfaction as the variables then click OK.
    2. Highlight the output and use Ctrl + C to copy and then Ctrl + V to past the output into this document.
  4. To create a histogram, select Graph from the menu and choose Histogram.
    1. Choose With Fit (makes it easier to see if the graph is left-skewed, symmetric or right-skewed). Then click OK.
    2. Choose Hosp_Stay and Hosp_Satisfaction and then click OK. Click on the graphs and use Ctrl+C to copy and use Ctrl+V to paste both graphs in the box below.
  5. Think about it: Why are so many items listed as missing? What can be done if you want to run the analysis and not include all those missing data values that occur at the end of the data set? Why does the satisfaction data have missing information before the end of the data set? Do we want to include the fact that those data values are missing in our analysis?
  6. Explorations – suggested activities but not required: Use table on page 99 (Chapter 4, Exhibit 4-5) as a reference point for analysis or do Class Activity # 3 on page 121 of your book.
  7. Deliverable: Save this document and submit it as Week_2_i-Lab_YourNameHere.docx to the Dropbox.


Central Tendency: Defined as statistics that describe the location of the distribution. This includes the mean, median, mode, and sum of all the values.

Mean. A measure of central tendency. The arithmetic average, the sum divided by the number of cases.

Median. The value above and below which half of the cases fall, the 50th percentile. If there is an even number of cases, the median is the average of the two middle cases when they are sorted in ascending or descending order. The median is a measure of central tendency not sensitive to outlying values (unlike the mean, which can be affected by a few extremely high or low values).

Mode. The most frequently occurring value. If several values share the greatest frequency of occurrence, each of them is a mode. The Frequencies procedure reports only the smallest of such multiple modes.

Minimum. The smallest value in the dataset.

Maximum. The largest value in the dataset.

S.E. Mean. A measure of how much the value of the mean may vary from sample to sample taken from the same distribution. It can be used to roughly compare the observed mean to a hypothesized value (that is, you can conclude the two values are different if the ratio of the difference to the standard error is less than –2 or greater than +2).

StDev. A measure of dispersion around the mean. In a normal distribution, 68% of cases fall within one standard deviation of the mean and 95% of cases fall within two standard deviations. For example, if the mean age is 45, with a standard deviation of 10, 95% of the cases would be between 25 and 65 in a normal distribution.

Variance (not included by default in Minitab).A measure of dispersion around the mean, equal to the sum of squared deviations from the mean divided by one less than the number of cases. The variance is measured in units that are the square of those of the variable itself.

Range The difference between the largest and smallest values of a numeric variable, the maximum minus the minimum.

Percentile Values (Q1, Median, Q3). Values of a quantitative variable that divide the ordered data into groups so that a certain percentage is above and another percentage is below. Quartiles (the 25th, 50th, and 75th percentiles) divide the observations into four groups of equal size default in Minitab). If you want an equal number of groups other than four, select Cut points for equal groups. You can also specify individual percentiles (for example, the 95th percentile, the value below which 95% of the observations fall).

IQR (Interquartile Range). This is the middle 50% of the data calculated by Q3 – Q1.

To include descriptive statistics that are not displayed by default, in the Display Descriptive Statistics dialog Box, select Statistics. In the dialog box that pops up, select the other items for descriptive statistics. Then click OK, OK