Design Resources Server

Analysis of Data from Designed Experiments

Latin Square Design


Example: An experiment was conducted at Agricultural Research Station, Kopurgaon, Maharashtra on cotton during the year 1969-1970 using a Latin Square Design to study the effects of foliar application of urea in combination with insecticidal sprays on the cotton yield. The 6 treatments were {T1 : Control (i. e. no N and no insecticides), T2 :100kg N/ha applied as urea (half at final thinning and half at flowering as top dressing), T3: 100kg N/ha applied as urea(80 kg N/ha In 4 equal split doses as spray and 20 kg N/ha at final  thinning), T4:100 kg. N/ha applied as CAN (half at final thinning and half at flowering as top dressing), T5 : T2 + six insecticidal sprays, T6 : T4 + six insecticidal sprays}. There were 6 replication, and the data of cotton in kg per plot is:





T3   3.10

T6     5.95

T1    1.75

T5      6.40

T2     3.85

T4    5.30

T2    4.80

T1     2.70

T3      3.30

T6    5.95

T4       3.70

T5      5.40

T1    3.00

T2     2.95

T5    6.70

T4    5.95

T6        7.75

T3    7.10

T5    6.40

T4    5.80

T2    3.80

T3      6.55

T1        4.80

T6      9.40

T6   5.20

T3    4.85

T4    6.60

T2     4.60

T5     7.00

T1      5.00

T4    4.25

T5    6.65

T6    9.30

T1    4.95

T3     9.30

T2       8.40




(i) Perform the analysis of the data and identify the best treatment.

(ii) Test whether the average effect of T3(100kg N/ha applied as urea) and T4 (100 kg N/ha ) is same as the average effect of T5(T2 + six insecticidal sprays) and T6(T4 + six insecticidal sprays).





Analysis Using SAS                                                                                       Analysis Using SPSS






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  

Contact Us  





Copyright        Disclaimer        How to Quote this page        Report Error        Comments/suggestions 

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)

For exposure on SAS, SPSS, MINITAB, SYSTAT and  
MS-EXCEL for analysis of data from designed experiments:

 Please see Module I of Electronic Book II: Advances in Data Analytical Techniques

available at Design Resources Server(