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This equation clearly shows the relationship between ndc and %SV and can be used to calculate the number of distinct categories for a given percentage study variation. As shown in Table 1, the calculated ndc value is then truncated to obtain a whole number (integer).
For example, if the calculated value is 15.8, mathematically you are not quite capable of differentiating between 16 categories. Therefore, Minitab Statistical Software is conservative and truncates the number of distinct categories to 15. For practical purposes, you can also round the calculated ndc values to obtain the number of distinct categories.
These guidelines have some limitations. For example, in some cases when the %SV is over 30% the number of distinct categories is 4. Therefore, a measurement system with 32% study variation, which is unacceptable under the AIAG criteria for %SV, is acceptable under the ndc criteria. To avoid this discrepancy, some authors suggest only accepting a measurement system when it can distinguish between 5 or more categories. Although this fixes the original problem, it makes measurement systems with a 28-30% study variation unacceptable, because their corresponding ndc value equals 4.
A lesser person might be intimidated as well as disgusted. That data has over 300 numbers in it. But not you. You're so good at Minitab that you know you don't have to type over 300 numbers. You can use that data just the way it is. First, enter the data into Minitab:
For the examples in this section, download the minitabintrodata.xlsx spreadsheet file. Save the file locally (if using Minitab installed on the computer you are using) or save the file in your PASS space if using WebApps.
Let's use Minitab to calculate the five number summary, mean and standard deviation for the Hours data, (contained in minitabintrodata.xlsx). And, as you will see, Minitab by default will provide some added information.
The mean, standard deviation (StDev), etc. should be the same values as those calculated in the practice problems. Minitab also gives the size of the sample used to create these statistics (N), and the number of observations from this data that were missing (N*).
This produces a list of integers takenat random [all values equally likely, each choice independentof others] from a specified range of values. (Useful for selectinga random sample from a listed population - as in using a randomnumber table, you will need to skip repeated values if you areusing this for sampling.)
Select Calc>Random Data>Integer
Type the number of random numbers (usually your sample size or a bit more) in the "Generate . . . rows of data" box.
Put the name of an unused column (you can see a list of the columns you are using) in the "Store in columns" box.
Put the smallest acceptable value in the " Minimum value " box, the largest acceptable value in the " Maximum value " box.
The other options for Calc>RandomData allow you to obtain random numbers following the variousdistributions named there.
In general, you will need to enter the parameters for the distributionyou select (n and p for binomial, mean and standard deviationfor normal, etc.)
When i googled the problem I found the following answer on the website where you can download minitab. "The asterisks represent missing values that cannot be calculated because the model is saturated and there are not enough degrees of freedom for error." Because of this i added a couple more values but im still getting asterisk instead of what the p-value and f-value are equal to.
My data is the number of video game soccer games I won that lasted different lengths. Half the games were only seen by me. The other half of the games were seen by me and someone else. The goal of the assignment is to see if there is an effect and/or interaction between how long a video game soccer game is, and how many games I win, as well to find out if there is an effect and/or interaction between how many games I win that are seen by just me and games that are seen by me and somebody else.
Each data set is stored in a column, designated by a"C" followed by a number. For example, C1 stands for Column1. The column designations are displayed along the top of theworksheet. The numbers at the left of the worksheet representpositions within a column and are referred to as rows. Eachrectangle occurring at the intersection of a column and a row iscalled a cell. It can hold one observation.
Example K: We can also use MINITAB to randomly select 5 from 100names in a hard-copy list. Assume the names are listed alphabetically, wherethe first name corresponds to the number 1 and the last corresponds to thenumber 100.
Minitab considers a data column as numerical as long asall entries in its cells are numbers. If one or more cells arenon-numerical (text, symbols), the entire data column isconsidered categorical, and the column label is changedfrom e.g. C2 to C2-T. Integer entries with spaces are interpretedas dates, and the column label is changed from C2 to C2-D.It can be tedious to undo such a change in data type, so becareful when entering data.
Go to Stat > Basic Statistics > Diplay DescriptiveStatistics.
Enter your desired variable(s) in Variable box.
Click OK.
The descriptive statistics (number of observations, mean, median,maximum, minimum, quartiles, standard deviation, and a fewothers) will appear in the Session window.
CDenotes a variable--written either as columns (C1, C12) or as names ('sex', 'age').KDenotes a constant--written either as a stored constant or as a number (3.54).EDenotes either a variable or a constant.MDenotes a matrix.[ ]Encloses an optional argument.
MINITAB variables are usually referred to by their column numbers (C1, C2) but the terms "column" or "variable" may be used interchangeably in actual usage or conceptualization. The variables may be named and the names can be used in the command sequence in place of column numbers.
MINITAB Constants are generally referred to as K1, K2, K3 (up to a maximum or 1000). The total number of stored constants is equal to the number of columns. Constants are not stored in the worksheets as Ks, so if you wish to retain constants in the worksheet for future sessions, they need to be stored in columns. These columns are then known as "artificially long" columns; the value appearing in the constant column is the same for every case. A possible use of a constant might be:
The mode is the most common number in any sample. If there is a tie, more than one mode is listed. Most people use the mode with numbers that describe categories such as types of soda or favorite sports. The mode indicates which category is the most popular. To calculate the mode by hand, count the number of votes for each category. When data gets to be too large to count by hand, use a statistical program like Minitab to calculate the mode.
Read the output, which displays in the session window. Minitab reports the date and time of the analysis followed by headers and numbers. The header "Variable Name" lists the name of the column of data you input into the spreadsheet. For this example, the name of the column is Favorite." The "Mode" heading is the actual value Minitab calculated for the mode. In this example, the mode is 7. The N for the Mode heading is the count of the most frequently occurring value; in this example, the value is 3, meaning three people preferred chocolate bar number 7.
Interpret the results. In this example, most of the people preferred chocolate bar number 7. You can have more than one mode. If the people surveyed liked chocolate bar number 7 and chocolate bar number 2 equally, the data would have two modes. Both numbers would be reported by Minitab with a comma between.
The Excel spreadsheet is also capable of calculating the t value for the p value and the number of DOF. So by inserting the p = 9.708 x 10-7 (cell B7) and DOF = 99 (cell C7), we get t = 5.24 (cell F7), as seen in Figure 3. Note: Excel rounds up cell B7 to 0.000001.
If you would like a copy of this spreadsheet, please send an email to me at rla...@indium.com.
Cheers,
Students explore the definition and interpretations of the probability of an event by investigating the long run proportion of times a sum of 8 is obtained when two balanced dice are rolled repeatedly. Making use of hand calculations, computer simulations, and descriptive techniques, students encounter the laws of large numbers in a familiar setting. By working through the exercises, students will gain a deeper understanding of the qualitative and quantitative relationships between theoretical probability and long run relative frequency. Particularly, students investigate the proximity of the relative frequency of an event to its probability and conclude, from data, that the dispersion of the relative frequency diminishes on the order .
Students should record this variable for n = 10, 25, 50, 100, 500, and 1000.
The instructor should draw six identical and parallel axes on a whiteboard, with the axes labeled n=10, n=25, n=50, n=100, n=500, and n=1000 (see Figure 3 for an example). As the students finish their simulations of 1000 rolls, each should come to the board and place a large dot at the appropriate place on each scale for their six values of Y. After all students have graphed their data, the result will be six side by side dotplots illustrating the distribution of the variable Y as a function of the number of rolls n. The dotplots may be easier to read and less prone to error if students convert their proportions to percents before placing dots on the board.
In addition to leading classroom discussions about the appearance of the six dotplots, the instructor should enter the six Y values (in decimal form) for each student in a spreadsheet so that students may have access to them for take-home investigations.