How to analyze or interpret the R could output (Model type, Model fit, Test the estimated parameters, Analys Morans test ). Below is the output. I am new to R could.

#Fitting a multivariate model

model1 <- lm(Newdata$Technical~ Newdata$Females +Newdata$Males)

#model summary

summary(model1)

## Call:

## lm(formula = Newdata$Technical ~ Newdata$Females + Newdata$Males)

## Residuals:

## Min 1Q Median 3Q Max

## -20.7803 -4.6382 -0.1126 3.9507 21.6656

## Coefficients:

## Estimate Std. Error t value Pr(>|t|)

## (Intercept) -3.28324 1.62300 -2.023 0.0438 *

## Newdata$Females 0.00335 0.02509 0.134 0.8938

## Newdata$Males 0.13509 0.02488 5.430 1.02e-07 ***

## ---

##
Signif. codes: 0 '**' 0.001 '**' 0.01 '' 0.05 '.' 0.1 ' ' 1

**' 0.001 '**' 0.01 '

## Residual standard error: 7.08 on 372 degrees of freedom

## Multiple R-squared: 0.3539, Adjusted R-squared: 0.3504

## F-statistic: 101.9 on 2 and 372 DF, p-value: < 2.2e-16

#Testing Spatial Autocorrelation

moran.lm<-lm.morantest(model1, W, alternative="two.sided")

print(moran.lm)

## Global Moran I for regression residuals

## data:

## model: lm(formula = Newdata$Technical ~ Newdata$Females +

## Newdata$Males)

## weights: W

## Moran I statistic standard deviate = 13.237, p-value < 2.2e-16

## alternative hypothesis: two.sided

## sample estimates:

## Observed Moran I Expectation Variance

2

## 0.415039220 -0.003097760 0.000997879

#LM multiplier

LM<-lm.LMtests(model1, W, test=c("LMerr", "LMlag",

"RLMerr", "RLMlag"))

print(LM)

## Lagrange multiplier diagnostics for spatial dependence

## data:

## model: lm(formula = Newdata$Technical ~ Newdata$Females +

## Newdata$Males)

## weights: W

## LMerr = 169.36, df = 1, p-value < 2.2e-16

## Lagrange multiplier diagnostics for spatial dependence

## data:

## model: lm(formula = Newdata$Technical ~ Newdata$Females +

## Newdata$Males)

## weights: W

## LMlag = 136.39, df = 1, p-value < 2.2e-16

## Lagrange multiplier diagnostics for spatial dependence

## data:

## model: lm(formula = Newdata$Technical ~ Newdata$Females +

## Newdata$Males)

## weights: W

## RLMerr = 34.051, df = 1, p-value = 5.37e-09

## Lagrange multiplier diagnostics for spatial dependence

## data:

## model: lm(formula = Newdata$Technical ~ Newdata$Females +

## Newdata$Males)

## weights: W

## RLMlag = 1.0859, df = 1, p-value = 0.2974

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