In our Manhattan model, we used 14 variables, so there are 14 coefficients:

[ -302.73009383 1199.3859951 4.79976742 -24.28993151 24.19824177 -7.58272473 -140.90664773 48.85017415 191.4257324 -151.11453388 89.408889 -57.89714551 -19.31948556 -38.92369828 ]

`bedrooms`

- number of bedrooms`bathrooms`

- number of bathrooms`size_sqft`

- size in square feet`min_to_subway`

- distance from subway station in minutes`floor`

- floor number`building_age_yrs`

- building’s age in years`no_fee`

- has no broker fee (0 for fee, 1 for no fee)`has_roofdeck`

- has roof deck (0 for no, 1 for yes)`has_washer_dryer`

- has in-unit washer/dryer (0/1)`has_doorman`

- has doorman (0/1)`has_elevator`

- has elevator (0/1)`has_dishwasher`

- has dishwasher (0/1)`has_patio`

- has patio (0/1)`has_gym`

- has gym (0/1)

To see if there are any features that don’t affect price linearly, let’s graph the different features against `rent`

.

**Interpreting graphs**

In regression, the independent variables will either have a positive linear relationship to the dependent variable, a negative linear relationship, or no relationship. A negative linear relationship means that as X values *increase*, Y values will *decrease*. Similarly, a positive linear relationship means that as X values *increase*, Y values will also *increase*.

Graphically, when you see a downward trend, it means a negative linear relationship exists. When you find an upward trend, it indicates a positive linear relationship. Here are two graphs indicating positive and negative linear relationships:

### Instructions

**1.**

Create a scatterplot of `size_sqft`

and `rent`

:

plt.scatter(df[['size_sqft']], df[['rent']], alpha=0.4)

Is there a strong correlation?

**2.**

Create a scatterplot of `min_to_subway`

and `rent`

:

plt.scatter(df[['min_to_subway']], df[['rent']], alpha=0.4)

Is there a strong correlation?

**3.**

Do the same for a few others and write down the ones that don’t have strong correlations.