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<<Back Analysis Using MS-EXCEL
To test whether the mean of the population of Seed yield/plant (g) is 200 or not one can use the following steps in MS-EXCEL: t = where n denotes the sample size.
and the sample standard deviation comes from the function “STDEV”.
Null hypothesis for this example is H0: mean=200, therefore the test value is 200. We now compute the value of test statistics as here n=20, sample mean =180.80, testvalue=200 and sample standard deviation = 37.31.
Computed t-value= -2.30; Modulus value
of the computed t-value is 2.30. 1. X is the modulus value of the computed t-value i.e., 2.30 2.
Type in the df = n – 1=20-1=19
Therefore the p-value= 0.03 To answer the question number 2 follow the following steps: · Once the data entry is complete, Choose Tools from the Menu Bar. Now select Tools → Data Analysis…
· For the analysis of t-Test: Two-Sample Assuming Unequal Variances, in the Data Analysis dialog box select t-Test: Two-Sample Assuming Equal Variance.
·
For the two groups select the variable
No. of fruit Set (45days)
and select the range for Variable 1 Range:
and Variable 2 Range:
in the Input box. Now select Output Range:
· Click OK to get the output at the selected output range.
· Similarly one can perform the analysis for the other variables also.
Analysis Using SAS Analysis Using SPSS Analysis Using MS-EXCEL
Home Descriptive Statistics Tests of Significance Correlation and Regression Completely Randomised Design RCB Design Incomplete Block Design Resolvable Block Design Augmented Design Latin Square Design Factorial RCB Design Partially Confounded Design Factorial Experiment with Extra Treatments Split Plot Design Strip Plot Design Response Surface Design Cross Over Design Analysis of Covariance Diagnostics and Remedial Measures Principal Component Analysis Cluster Analysis Groups of Experiments Non-Linear Models
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Descriptive Statistics | |||||
Tests of Significance | |||||
Correlation and Regression | |||||
Completely Randomised Design | |||||
RCB Design | |||||
Incomplete Block Design | |||||
Resolvable Block Design | |||||
Augmented Design | |||||
Latin Square Design | |||||
Factorial RCB Design | |||||
Partially Confounded Design | |||||
Factorial Experiment with Extra Treatments | |||||
Split Plot Design | |||||
Strip Plot Design | |||||
Response Surface Design | |||||
Cross Over Design | |||||
Analysis of Covariance | |||||
Diagnostics and Remedial Measures | |||||
Principal Component Analysis | |||||
Cluster Analysis | |||||
Groups of Experiments | |||||
Non-Linear Models | |||||
Contact Us | |||||
Other
Designed Experiments (Under Development) |
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For
exposure on SAS, SPSS, Please see Module I of Electronic Book II: Advances in Data Analytical Techniques available at Design Resource Server (www.iasri.res.in/design) |
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