Minitab allows you to quickly enter your data and run a variety of analyses on it.It is possible to quickly prepare charts and calculate regression, and enter data in the same way as in excel.Minitab takes a lot of the hard work out of statistics calculations.
Step 1: The Minitab layout is new to you.
You will be shown two main windows when you first start Minitab.The output of any analysis will be displayed in the Session window, while the data entry window is where you enter your data.The window will look similar to an excel spreadsheet.
Step 2: The data labels are in the second row.
The C1, C2, C3 are reserved for the first row.Minitab assigns labels to the columns.You can manually enter column labels in the second row.Click an empty second row cell and type in the label for that column.
Step 3: Your data can be entered into columns.
You can start entering data once you have your column labeled.You can go to the cell below the current one by pressing Enter.If you click the little arrow in the upper- left corner, you can change the direction of the data entry so that it takes you to the next column on the same row.You can copy and paste data from an excel spreadsheet to Minitab.You can highlight the data set in excel.Click the mouse to copy it.Click the first cell below C1 if you want to go to Minitab.Click the mouse to select paste cells.There should be one type of data in each column.For example, if you are entering information about baseball teams, one column might be home runs.
Step 4: Descriptive statistics are what they are.
A set of data is summarized using several important values.Mean - Average value of the data in the column Standard deviation - Measure of dispersion of data Median - The smallest number in a set
Step 5: You can click on the Stat menu.
Click the menu at the top of the window to enter the data set.You can hover your mouse over Basic Statistics.
Step 6: You can display Descriptive Statistics.
You can see all of your columns in a list on the left and a Variables box in the right when you open the Display Descriptive Statistics window.
Step 7: Click on the variable to analyze it.
The Variables box can be found on the right side of the window.
Step 8: You can choose the statistics you want to see.
To choose which statistics you want to display, click Statistics...You have the option to check or remove any of the boxes.When you've made a decision, click OK.
Step 9: The output should be read.
Once you're satisfied with the data set and statistics options, click OK in the display Descriptive Statistics window.You will see the descriptive statistics when you open your Session window.
Step 10: A histogram is created.
The graph has frequencies for categories.You can see the number of times a variable occurs.Click on the Graph menu.Click the menu at the top of the window to enter the data set.Select histogram...Select the type of graph you want."With Fit and Groups" is one of the four options for creating a histogram."Simple" is what you should choose.Pick your data set.You can see a list of available data sets.Click OK to double-click the one you want to create the histogram from.Your histogram will be displayed in a new window.
Step 11: There is a dot plot.
A dot plot is similar to a histogram in that it shows the values in different categories.It's best for small sets of data.Click on the Graph menu.Click the menu at the top of the window to enter the data set.Select a plot...You can choose your graph type.When creating a dot plot, you are given seven options.To create a dot plot from a single column of data, select Simple.Pick your data set.You will see a list of data sets.Click OK if you want to create the dot plot from the other one.The dot plot will appear in a new window.
Step 12: A stem-and-leaf plot can be created.
The plot is similar to a histogram.The frequencies at which values occur are shown.There is no visual aspect to the numbers shown in each category.You can click on the Graph menu.Click the menu at the top of the window after entering the data set.Select Stem-and-Leaf...Pick your data set.You will see a list of data sets.Click OK if you want to create the stem-and-leaf from one of the other ones.The stem-and-leaf plot will appear in the Session window.Stem-and-leaf plots can be seen in this guide.
Step 13: There is a probability plot.
You can quickly identify outliers and other departures from a normal curve with this plot.Click on the Graph menu.Click the menu at the top of the window to enter the data set.Pick the Probability Plot...You can choose a graph type.There are two options for making a probability plot.For now, choose single.Pick your data set.You can see a list of available data sets.Click OK if you want to create the probability plot.A new window will show your probability plot.
Step 14: A bar chart can be created.
A bar chart shows your data.Columns in bar charts represent categorical variables, while columns in a histogram represent quantitative variables.You can click on the Graph menu.Click the menu at the top of the window after entering the data set.Select the bar chart.What bars do you want to represent?The bars should represent counts of unique values, a function of a variable, or values from a table, if you use the drop-down menu.You can choose your chart type.You will usually choose the Simple bar chart.You can choose your data set.You can see a list of available data sets.Click the one you want to create the bar chart from.Click the Labels... button to add labels to your chart.The bar chart can be created in a new window.
Step 15: You can create a pie chart.
A pie chart is like a bar chart in that it shows categorical variables.You can click on the Graph menu.Click the menu at the top of the window after entering the data set.Pie chart...Pick your data set.You can see a list of available data sets.Click the one you want to create the pie chart from.You can click the button to add labels.In a new window, click OK to build the pie chart.
Step 16: Understand what a regression analysis is.
Random variables and regression analysis models.There are two types of variables in regression analysis.Values of predictor variables are chosen to predict the values of response variables, and the regression analysis will determine how accurate this prediction tends to be.The response variable and predictor variable are usually represented by Y and X.
Step 17: You should create a data set.
In separate columns, enter response and predictor variables.The columns need to be labeled in the second row.The variable was measured in an experiment.It's also called dependent variable.Predictor Variables determine the change of other variables.They're also called independent variables.
Step 18: The wizard can be opened.
Click the menu and then select Regression.
Step 19: You should add your variables.
Click on the data set that is your "Response", or "dependency" variable.It will be added to the "Response" field.Double-click the data set that is your "Predictor", or "independent" variable.This will be added to the "Predictors" field.Multiple variables can be added to the "Predictors" field.
Step 20: You can choose any graphs.
Clicking the Graphs... button will let you generate graphs with your analysis.You can choose which graphs you want to make.After you make your selections, click OK.
Step 21: You can choose to store results.
You can store your results in Minitab.You can choose what aspects are stored with the Storage button.These will be added to your spreadsheet.
Step 22: The regression analysis can be run.
Click OK when you're done configuring your options.You can use Minitab to calculate the regression and display charts and stored values.The session window of Minitab has the output from the regression analysis.The significance of predictor variables is determined by the P- values of the regression equation.R-sq shows how well the data fits the model.