Minitab Course Help by Minitab Experts at My Course Help
Minitab provides a simple, effective way to input statistical data, manipulate that data, identify trends and patterns, and then extrapolate answers to the problem at hand. That’s a rather simplistic way of describing this vital tool, but it is effective. This software option uses a series of elements to help Six Sigma professionals work with data and statistics. For instance, it includes boxplots, scatterplots, and histograms and provides the ability to calculate "descriptive statistics". Today, Minitab is often used in conjunction with the implementation of Six Sigma, CMMI, and other statistics-based process improvement methods. Minitab 17, the latest version of the software, is available in 8 languages: English, French, German, Japanese, Korean, Portuguese, Simplified Chinese, & Spanish.
Data Types in Minitab Homework Help Online
Numerical: Numerical data is the only type Minitab will use for statistical calculations. Numerical data is aligned on the right side of the column. Minitab will not recognize numbers with commas as numbers but will consider the text.
Text: Text cannot be used for computations. Though "text" generally means words or characters, numbers can be classified as text. If column 1 has text in it, the column label will change from C1 to C1-T. Data types can be changed. See the details in the Manipulating Data section.
Date/Time: Minitab recognizes 3/5/00 as a date and 4:30 as time but will store these internally as a number so you can manipulate them. The column label will indicate a date or time value by putting a D after the column name.
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Sample Minitab Assignment Solved by Our MINITAB Course Help Expert
Project Write-Up
Once your topic has been approved and you have collected your data, you are ready to do your report.
- Write a paragraph describing your project:
- Be sure to explain your research question - what exactly are you interested in analyzing? Why did you choose this particular topic?
- Next, describe the method that you used to collect your data. How difficult was your search? Are you satisfied that the site(s) that you used is valid?
- List links to all the sites that you used so that I can verify your data.
- If you found some flaws or problems that you ran into with your collection of data, discuss those problems.
- Is your data a sample or is it the entire population. If it is a sample, how did you select your sample?
Include a print-out of your data in Minitab with the Columns labeled. Make sure that one column includes names. For example, suppose you are looking at obesity rates by state for 2010 and 2018. One column should include the names of each state and the next two columns should include the data. If you are looking at MLB teams’ salaries and winning percentages, the first column should be the team names. (Note: If you are looking at data such as winning percentages and salaries, please enter the salaries in millions and tenths of millionths. For example, if a team’s total salary is $111,452,000, enter it into MINITAB as 111.5 and label the column as Salary ($millions).
- Minitab Graphs of your data:
- Create a Boxplot for each column of numerical data. If the two columns of data are similar sets of data (for example, obesity rates in 2010 and in 2018), put both boxplots on the same graph.
- Create a Scatterplot of your data. Make sure you have placed the variables on the correct axes. The predictor variable belongs on the x-axis and the response variable belongs on the y-axis.
- Numerical summaries: For your data set, include a 5-number summary, the mean and the standard deviation, done in Minitab for each column of numerical data. Do a test for outliers using the IQR rule for each column of data. (See Notebook pg. 45.) >Write out all the calculations (by hand) for the outlier test and list all your outliers. If you have any outliers make sure that you discuss them. What are they telling you? Maybe there is a state where the obesity rate changed dramatically and that state is an outlier. So, talk about that state and mention it when you do this outlier test. If the outlier is a particular player on a professional team, maybe you might have an idea why he/she is an outlier.
- Regression Analysis: Look at your scatterplot and decide on a model for your data (linear or quadratic). Do your regression and include the Minitab output with your regression equation and scatterplot and the R-Sq value. If you think there are any unusual values, circle them on your scatterplot.
In this section of your report, discuss the regression model. Does the model look like a good fit for the data? Why or why not? What does the R-Sq value tell you about the usefulness of the model? Did you find any values that looked unusual? Discuss them.
Do a scatterplot of the standardized residuals vs. x-values. Include this scatterplot with your report and explain its meaning. Note: For instructions on Regression Analysis, including a scatterplot of standardized residuals, refer to Chapter 11 in your MINITAB manual.
In this section of your report, discuss the residual plot. What does the plot look like and what does that tell you about the model that you chose? Circle all standardized residuals that are smaller than -2 or bigger than +2. What data values do these residuals represent?
Conclusion: Write a paragraph summarizing what you learned from your data. Make this an interesting summary!! It is important that you understand what your analysis tells you about the relationship between the 2 variables. Include comments from the graphs and from the numerical summaries of the data.
Note: Make sure that you understand your data. If you are looking at something like divorce rates by state, those values are often given as divorces per 1000 marriages. So maybe you will see ‘6.4’ as a data value. That means 6.4 divorces per 1000 marriages. It is important that you talk about the data correctly. You don’t want to say that the average divorce rate is 6.4 because that makes no sense. It is 6.4 per 1000 marriages.
Please number each part of your project according to the numbering above. Please do not hand in any handwritten work (other than the mathematical calculations for the outlier test.) All graphs must be done in Minitab.
MINITAB Course Help Sample Answer Solved by the Experts
- Minitab Graphs – see #3 above – 15 points
The graph shows that there is a positive linear relationship between the poverty rate and the obesity rate
- Numerical summaries and Outlier Tests – see #4 above – 15 points
poverty rates |
Child obesity rates |
||
Moyenne |
15.68% |
Moyenne |
15.05% |
Erreur-type |
0.85% |
Erreur-type |
0.44% |
Mdiane |
15.20% |
Mdiane |
15.10% |
Mode |
11.40% |
Mode |
15.60% |
cart-type |
6.05% |
cart-type |
3.17% |
Variance de l'chantillon |
0.003654881 |
Variance de l'chantillon |
0.00100509 |
Kurstosis (Coefficient d'aplatissement) |
27.33285917 |
Kurstosis (Coefficient d'aplatissement) |
1.68581921 |
Coefficient d'asymtrie |
4.55406226 |
Coefficient d'asymtrie |
0.6073116 |
Plage |
43.10% |
Plage |
17.40% |
Minimum |
9.20% |
Minimum |
8.70% |
Maximum |
52.30% |
Maximum |
26.10% |
Somme |
7.997 |
Somme |
7.676 |
Nombre d'chantillons |
51 |
Nombre d'chantillons |
51 |