Multi-linear Regression

            Each district had a distinct combination of climate variables for SPR and API that produced the largest R2 value for that set of predictor variables.  Below are displayed the group of climate variables for each district that, together in a multi-linear regression, explained the most variability (highest R2 values) in API and SPR by climatic variables alone. 

 

BEST REGRESSION FOR API BY DISTRICT IN KARNATAKA, INDIA

 

 

 

   

 

 

 

Temperature

 

 

 

 

 

Precipitation

 

 

R^2

p>F

Coastal

 

 

 

 

 

 

 

 

 

 

 

 

 

Uttara Kannara

Jan

Feb

March

May

Oct

Lag Dec

Lag Nov

 

 

0.67

0.07

Dakshina Kannara  

Lag Dec

 

 

 

 

 

July

Lag Nov

 

 

0.30

0.03

North Interior

 

   

   

 

 

 

   

  

   

 

 

 

   

Raichur  

Jan

Feb

   

 

 

 

   

April

   

 

 

0.53

0.002

Gulbarga

Jan

Feb

Lag Nov

 

 

 

 

Nov

Lag Nov

 

 

0.50

0.009

Bidar

Jan

Lag Nov

 

 

 

 

 

June

 

 

 

0.51

0.002

Belgaum  

Jan

April

May

Lag Nov

 

   

Lag Nov  

 

 

0.66

0.001

Bijapur  

Jan

April

May

   

 

 

  

   

   

 

 

0.36

0.02

 

Dharwad

April

May  

July  

Sept

Dec

Lag Nov

Lag Dec 

April  

Lag Nov 

 

   

0.87

0.0001

 

South Interior

 

 

 

 

 

 

 

 

 

 

 

 

 

Bellary

Jan

April

May

July

Feb

Lag Nov

Lag Dec

Lag Nov

 

 

0.84

0.0001

Shimoga

April

May

Lag Nov

Lag Dec

 

 

April

Nov

Lag Nov

0.87

0.0001

Chitradurga and Hassan

April

May

July

Lag Dec

 

 

April

Lag Nov

 

 

0.70

0.0007

Kodagu  

May

   

   

 

 

 

   

Oct

Nov

Lag Nov

0.63

0.003

Bangalore

July

   

   

 

 

 

   

April

Lag Nov

Feb

 

0.47

0.008

Mandya

 

 

 

 

 

 

 

Apri

Lag Nov

Feb

 

0.59

0.002

Chikmagalur

April

July

Lag Dec

 

 

 

 

April

July

Oct

Lag Nov

0.77

0.0004

Mysore

May

Lag Nov

Lag Dec

 

 

 

 

April

Nov

 

 

0.62

0.002

Tumkur  

Lag Dec  

   

 

 

 

  

May

Nov

Lag Nov

0.67

0.002

Kolar

Feb