mean_presc  deprivation
count   202.000000  202.000000
mean    1.108072    106.306931
std     0.139628    60.217804
min     0.641107    1.000000
25%     1.033221    54.250000
50%     1.119174    107.500000
75%     1.206066    158.750000
max     1.449776    209.00000

mean_presc  deprivation
0   1.074778    121
1   1.209622    144
2   1.106975    193
3   1.098038    16
4   0.906051    136

y=depr_mean["mean_presc"]
X=depr_mean["deprivation"]
model = sm.OLS(y,X).fit()
print (model.summary())

>    OLS Regression Results
==============================================================================
Dep. Variable:             mean_presc   R-squared:                       0.716
Model:                            OLS   Adj. R-squared:                  0.715
Method:                 Least Squares   F-statistic:                     507.8
Date:                Wed, 20 Jun 2018   Prob (F-statistic):           6.50e-57
Time:                        22:46:44   Log-Likelihood:                -181.65
No. Observations:                 202   AIC:                             365.3
Df Residuals:                     201   BIC:                             368.6
Df Model:                           1
Covariance Type:            nonrobust
===============================================================================
                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------
deprivation     0.0077      0.000     22.535      0.000       0.007       0.008
==============================================================================
Omnibus:                       29.635   Durbin-Watson:                   1.541
Prob(Omnibus):                  0.000   Jarque-Bera (JB):                7.996
Skew:                           0.065   Prob(JB):                       0.0184
Kurtosis:                       2.034   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

library (dplyr)
depr_mean<-read.csv("depr_mean.csv")
summary (depr_mean)

X            mean_presc      deprivation
 Min.   :  0.00   Min.   :0.6411   Min.   :  1.00
 1st Qu.: 50.25   1st Qu.:1.0332   1st Qu.: 54.25
 Median :100.50   Median :1.1192   Median :107.50
 Mean   :100.50   Mean   :1.1081   Mean   :106.31
 3rd Qu.:150.75   3rd Qu.:1.2061   3rd Qu.:158.75
 Max.   :201.00   Max.   :1.4498   Max.   :209.00

    head(depr_mean)
X   mean_presc  deprivation
0   1.0747779   121
1   1.2096218   144
2   1.1069754   193
3   1.0980376   16
4   0.9060512   136
5   1.2064052   39

model_pgr <- lm (mean~IMD, data=depr_desc)
summary (model_pgr)

Call:
lm(formula = mean ~ IMD, data = depr_desc)

Residuals:
     Min       1Q   Median       3Q      Max
-0.49150 -0.05094  0.01817  0.08530  0.27441

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  1.1779952  0.0191975  61.362  < 2e-16 ***
IMD         -0.0006578  0.0001572  -4.184 4.29e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1342 on 200 degrees of freedom
Multiple R-squared:  0.08047,   Adjusted R-squared:  0.07587
F-statistic:  17.5 on 1 and 200 DF,  p-value: 4.294e-05