A - Setup
- Open your
baselrbootcamp
R project. It should already have the folders 1_Data
and 2_Code
. Make sure that the data files listed in the Datasets
section above are in your 1_Data
folder.
# Done!
- Open a new R script. At the top of the script, using comments, write your name and the date. Save it as a new file called
analysing_practical.R
in the 2_Code
folder.
Using library()
load the set of packages for this practical listed in the packages section above.
For this practical, we’ll use the kc_house.csv
data. This dataset contains house sale prices for King County, Washington. It includes homes sold between May 2014 and May 2015. Using the following template, load the data into R and store it as a new object called kc_house
.
kc_house <- read_csv(file = "XX")
- Using
print()
, summary()
, and head()
, explore the data to make sure it was loaded correctly.
kc_house
# A tibble: 21,613 x 21
id date price bedrooms bathrooms sqft_living
<chr> <dttm> <dbl> <int> <dbl> <int>
1 7129300520 2014-10-13 00:00:00 221900 3 1 1180
2 6414100192 2014-12-09 00:00:00 538000 3 2.25 2570
3 5631500400 2015-02-25 00:00:00 180000 2 1 770
4 2487200875 2014-12-09 00:00:00 604000 4 3 1960
5 1954400510 2015-02-18 00:00:00 510000 3 2 1680
6 7237550310 2014-05-12 00:00:00 1225000 4 4.5 5420
7 1321400060 2014-06-27 00:00:00 257500 3 2.25 1715
8 2008000270 2015-01-15 00:00:00 291850 3 1.5 1060
9 2414600126 2015-04-15 00:00:00 229500 3 1 1780
10 3793500160 2015-03-12 00:00:00 323000 3 2.5 1890
# ... with 21,603 more rows, and 15 more variables: sqft_lot <int>,
# floors <dbl>, waterfront <int>, view <int>, condition <int>,
# grade <int>, sqft_above <int>, sqft_basement <int>, yr_built <int>,
# yr_renovated <int>, zipcode <int>, lat <dbl>, long <dbl>,
# sqft_living15 <int>, sqft_lot15 <int>
summary(kc_house)
id date price
Length:21613 Min. :2014-05-02 00:00:00 Min. : 75000
Class :character 1st Qu.:2014-07-22 00:00:00 1st Qu.: 321950
Mode :character Median :2014-10-16 00:00:00 Median : 450000
Mean :2014-10-29 04:38:01 Mean : 540088
3rd Qu.:2015-02-17 00:00:00 3rd Qu.: 645000
Max. :2015-05-27 00:00:00 Max. :7700000
bedrooms bathrooms sqft_living sqft_lot
Min. : 0.0 Min. :0.00 Min. : 290 Min. : 520
1st Qu.: 3.0 1st Qu.:1.75 1st Qu.: 1427 1st Qu.: 5040
Median : 3.0 Median :2.25 Median : 1910 Median : 7618
Mean : 3.4 Mean :2.11 Mean : 2080 Mean : 15107
3rd Qu.: 4.0 3rd Qu.:2.50 3rd Qu.: 2550 3rd Qu.: 10688
Max. :33.0 Max. :8.00 Max. :13540 Max. :1651359
floors waterfront view condition
Min. :1.00 Min. :0.000 Min. :0.00 Min. :1.00
1st Qu.:1.00 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:3.00
Median :1.50 Median :0.000 Median :0.00 Median :3.00
Mean :1.49 Mean :0.008 Mean :0.23 Mean :3.41
3rd Qu.:2.00 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:4.00
Max. :3.50 Max. :1.000 Max. :4.00 Max. :5.00
grade sqft_above sqft_basement yr_built
Min. : 1.00 Min. : 290 Min. : 0 Min. :1900
1st Qu.: 7.00 1st Qu.:1190 1st Qu.: 0 1st Qu.:1951
Median : 7.00 Median :1560 Median : 0 Median :1975
Mean : 7.66 Mean :1788 Mean : 292 Mean :1971
3rd Qu.: 8.00 3rd Qu.:2210 3rd Qu.: 560 3rd Qu.:1997
Max. :13.00 Max. :9410 Max. :4820 Max. :2015
yr_renovated zipcode lat long
Min. : 0 Min. :98001 Min. :47.2 Min. :-122
1st Qu.: 0 1st Qu.:98033 1st Qu.:47.5 1st Qu.:-122
Median : 0 Median :98065 Median :47.6 Median :-122
Mean : 84 Mean :98078 Mean :47.6 Mean :-122
3rd Qu.: 0 3rd Qu.:98118 3rd Qu.:47.7 3rd Qu.:-122
Max. :2015 Max. :98199 Max. :47.8 Max. :-121
sqft_living15 sqft_lot15
Min. : 399 Min. : 651
1st Qu.:1490 1st Qu.: 5100
Median :1840 Median : 7620
Mean :1987 Mean : 12768
3rd Qu.:2360 3rd Qu.: 10083
Max. :6210 Max. :871200
head(kc_house)
# A tibble: 6 x 21
id date price bedrooms bathrooms sqft_living sqft_lot
<chr> <dttm> <dbl> <int> <dbl> <int> <int>
1 7129… 2014-10-13 00:00:00 2.22e5 3 1 1180 5650
2 6414… 2014-12-09 00:00:00 5.38e5 3 2.25 2570 7242
3 5631… 2015-02-25 00:00:00 1.80e5 2 1 770 10000
4 2487… 2014-12-09 00:00:00 6.04e5 4 3 1960 5000
5 1954… 2015-02-18 00:00:00 5.10e5 3 2 1680 8080
6 7237… 2014-05-12 00:00:00 1.23e6 4 4.5 5420 101930
# ... with 14 more variables: floors <dbl>, waterfront <int>, view <int>,
# condition <int>, grade <int>, sqft_above <int>, sqft_basement <int>,
# yr_built <int>, yr_renovated <int>, zipcode <int>, lat <dbl>,
# long <dbl>, sqft_living15 <int>, sqft_lot15 <int>
B - Recap
- Print the names of the
kc_house
data with names()
.
names(kc_house)
[1] "id" "date" "price" "bedrooms"
[5] "bathrooms" "sqft_living" "sqft_lot" "floors"
[9] "waterfront" "view" "condition" "grade"
[13] "sqft_above" "sqft_basement" "yr_built" "yr_renovated"
[17] "zipcode" "lat" "long" "sqft_living15"
[21] "sqft_lot15"
- Change the following column names using
rename()
.
living_sqft |
sqft_living |
lot_sqft |
sqft_lot |
above_sqft |
sqft_above |
basement_sqft |
sqft_basement |
built_yr |
yr_built |
renovated_yr |
yr_renovated |
- Create new column(s)
living_sqm
, lot_sqm
, above_sqm
and basement_sqm
which show the respective room sizes in square meters rather than square feet (Hint: Multiply each by 0.093).
kc_house <- kc_house %>%
mutate(living_sqm = XXX * XXX,
lot_sqm = XXX * XXX,
XXX = XXX,
XXX = XXX)
kc_house <- kc_house %>%
mutate(living_sqm = living_sqft * 0.093,
lot_sqm = lot_sqft * 0.093,
above_sqm = above_sqft * 0.093,
basement_sqm = basement_sqft * 0.093)
- Add a new variable to the dataframe called
mansion
which is “Yes” when the sum of the house’s living, above, and basement space is above 750 square meters.
kc_house <- kc_house %>%
mutate(mansion = case_when(
living_sqm + above_sqm + basement_sqm > 750 ~ "Yes",
living_sqm + above_sqm + basement_sqm <= 750 ~ "No"))
C - Simple summaries
- Using the base-R
df$col
notation, calculate the mean price of all houses.
mean(XXX$XXX)
mean(kc_house$price)
[1] 540088
- Now, do the same using
summarise()
using the following template. Do you get the same answer? What is different about the output from summarise()
versus using the dollar sign?
kc_house %>%
summarise(
price_mean = mean(XXX)
)
kc_house %>%
summarise(
price_mean = mean(price)
)
# A tibble: 1 x 1
price_mean
<dbl>
1 540088.
- What is the median price of all houses? Use the
median()
function!
kc_house %>%
summarise(
price_median = median(price)
)
# A tibble: 1 x 1
price_median
<dbl>
1 450000
- What was the highest selling price? Use the
max()
function!
kc_house %>%
summarise(
price_max = max(price)
)
# A tibble: 1 x 1
price_max
<dbl>
1 7700000
- Using the following template, sort the data frame in descending order of price. Then, print it. Do you see the house with the highest selling price at the top?
kc_house <- kc_house %>%
arrange(desc(XXX))
kc_house
kc_house <- kc_house %>%
arrange(desc(price))
kc_house
# A tibble: 21,613 x 26
id date price bedrooms bathrooms living_sqft
<chr> <dttm> <dbl> <int> <dbl> <int>
1 6762700020 2014-10-13 00:00:00 7700000 6 8 12050
2 9808700762 2014-06-11 00:00:00 7062500 5 4.5 10040
3 9208900037 2014-09-19 00:00:00 6885000 6 7.75 9890
4 2470100110 2014-08-04 00:00:00 5570000 5 5.75 9200
5 8907500070 2015-04-13 00:00:00 5350000 5 5 8000
6 7558700030 2015-04-13 00:00:00 5300000 6 6 7390
7 1247600105 2014-10-20 00:00:00 5110800 5 5.25 8010
8 1924059029 2014-06-17 00:00:00 4668000 5 6.75 9640
9 7738500731 2014-08-15 00:00:00 4500000 5 5.5 6640
10 3835500195 2014-06-18 00:00:00 4489000 4 3 6430
# ... with 21,603 more rows, and 20 more variables: lot_sqft <int>,
# floors <dbl>, waterfront <int>, view <int>, condition <int>,
# grade <int>, above_sqft <int>, basement_sqft <int>, built_yr <int>,
# renovated_yr <int>, zipcode <int>, lat <dbl>, long <dbl>,
# sqft_living15 <int>, sqft_lot15 <int>, living_sqm <dbl>,
# lot_sqm <dbl>, above_sqm <dbl>, basement_sqm <dbl>, mansion <chr>
- What percentage of houses sold for more than 1,000,000? Let’s answer this with
summarise()
.
kc_house %>%
summarise(mil_p = mean(XXX > 1000000))
kc_house %>%
summarise(mil_p = mean(price > 1000000))
# A tibble: 1 x 1
mil_p
<dbl>
1 0.0678
- For mansions only, calculate the mean number of floors and bathrooms (hint: before summarising the data, use
filter()
to only select mansions!)
kc_house %>%
filter(mansion == "Yes") %>%
summarise(
floors_mean = mean(floors),
bathrooms_mean = mean(bathrooms)
)
# A tibble: 1 x 2
floors_mean bathrooms_mean
<dbl> <dbl>
1 1.92 3.68
D - Simple grouped summaries
- How many mansions are there? To do this, use
group_by()
to group the dataset by the mansions
column, then use the n()
function to count the number of cases.
kc_house %>%
group_by(XXX) %>%
summarise(XXX = n())
kc_house %>%
group_by(mansion) %>%
summarise(N = n())
# A tibble: 2 x 2
mansion N
<chr> <int>
1 No 20862
2 Yes 751
- What is the mean selling price of mansions versus non-mansions? To do this, just add another argument to your
summarise()
function!
kc_house %>%
group_by(mansion) %>%
summarise(N = n(),
XXX = XXX(XXX))
kc_house %>%
group_by(mansion) %>%
summarise(N = n(),
price_mean = mean(price))
# A tibble: 2 x 3
mansion N price_mean
<chr> <int> <dbl>
1 No 20862 504024.
2 Yes 751 1541915.
- Using
group_by()
and summarise()
, create a dataframe showing the same results as the following table.
No |
20862 |
75000 |
504024 |
441000 |
3100000 |
Yes |
751 |
404000 |
1541915 |
1300000 |
7700000 |
- Do houses built in later years tend to have more living space? To answer this, group the data by
built_yr
, and then calculate the mean number of living square meters. Be sure to also include the number of houses built in each year!
kc_house %>%
group_by(built_yr) %>%
summarise(N = n(),
living = mean(living_sqm))
# A tibble: 116 x 3
built_yr N living
<int> <int> <dbl>
1 1900 87 161.
2 1901 29 164.
3 1902 27 179.
4 1903 46 140.
5 1904 45 149.
6 1905 74 183.
7 1906 92 168.
8 1907 65 177.
9 1908 86 158.
10 1909 94 177.
# ... with 106 more rows
- Was that table too big? Try using the following code to get the results for each decade rather than each year!
kc_house %>%
mutate(built_decade = floor(built_yr / 10)) %>%
group_by(built_decade) %>%
summarise(XX = XX,
XX = XX(XX))
- A friend of yours who is getting into Seattle real estate wants to know how the number of floors a house has affects its selling price. Create a table for her showing the minimum, mean, and maximum price for houses separated by the number of floors they have.
E - Multiple groups
- Your friend Brumhilda is interested in statistics on houses in the following 4 zipcodes only: 98001, 98109, 98117, 98199. Create a new dataframe called
brumhilda
that only contains data from houses in those zipcode (hint: use filter()
combined with the %in%
operator as follows:
brumhilda <- kc_house %>%
filter(XXX %in% c(XXX, XXX, XXX, XXX))
brumhilda <- kc_house %>%
filter(zipcode %in% c(98001, 98109, 98117, 98199))
- For each of the zip codes, calculate the
mean()
and median()
selling price (as well as the number of houses) in each zip code.
brumhilda %>%
group_by(zipcode) %>%
summarise(price_mean = mean(price),
price_median = median(price),
N = n())
# A tibble: 4 x 4
zipcode price_mean price_median N
<int> <dbl> <dbl> <int>
1 98001 280805. 260000 362
2 98109 879624. 736000 109
3 98117 576795. 544000 553
4 98199 791821. 689800 317
- Now Brumhilda wants the same data separated by whether or not the house is a mansion or not. Include these results by also grouping the data by
mansion
(as well as zipcode
), and calculating the same summary statistics as before.
brumhilda %>%
group_by(zipcode, mansion) %>%
summarise(price_mean = mean(price),
price_median = median(price),
N = n())
# A tibble: 8 x 5
# Groups: zipcode [?]
zipcode mansion price_mean price_median N
<int> <chr> <dbl> <dbl> <int>
1 98001 No 277589. 260000 359
2 98001 Yes 665667. 637000 3
3 98109 No 833528. 730500 106
4 98109 Yes 2508333. 2900000 3
5 98117 No 575626. 543000 551
6 98117 Yes 898750 898750 2
7 98199 No 753625. 675000 305
8 98199 Yes 1762618. 1425000 12
- Ok that was good, but now she also wants to know what the maximum and minimum number of floors were in each group. Add these summary statistics!
brumhilda %>%
group_by(zipcode) %>%
summarise(price_mean = mean(price),
price_median = median(price),
floors_min = min(floors),
floors_max = max(floors),
N = n())
# A tibble: 4 x 6
zipcode price_mean price_median floors_min floors_max N
<int> <dbl> <dbl> <dbl> <dbl> <int>
1 98001 280805. 260000 1 2.5 362
2 98109 879624. 736000 1 3 109
3 98117 576795. 544000 1 3 553
4 98199 791821. 689800 1 3 317
F - Statistics
- Let’s see if there is a significant difference between the selling prices of houses on the waterfront versus those not on the waterfront. To do this, we’ll conduct a t-test using the
t.test()
function and assign the result to waterfront_htest
. Fill in the XXXs in the code below, such that the dependent variable is price
and the independent variable is waterfront
waterfront_htest <- t.test(formula = XXX ~ XXX,
data = XXX)
waterfront_htest <- t.test(formula = price ~ waterfront,
data = kc_house)
- Print your
waterfront_htest
object to see a printout of the main results.
waterfront_htest
Welch Two Sample t-test
data: price by waterfront
t = -10, df = 200, p-value <2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1303662 -956963
sample estimates:
mean in group 0 mean in group 1
531564 1661876
- Look at the names of your
waterfront_htest
object with names()
.
names(waterfront_htest)
[1] "statistic" "parameter" "p.value" "conf.int" "estimate"
[6] "null.value" "alternative" "method" "data.name"
- Using the
$
, print the test statistic (statistic
) from your waterfront_htest
object.
waterfront_htest$statistic
t
-12.9
- Now using
$
, print only the p-value (p.value
) from the object.
waterfront_htest$p.value
[1] 1.38e-26
- Which of the variables
bedrooms
, bathrooms
, living_sqm
, waterfront
, lat
and long
best predict a house’s price (price
)? Answer this by conducting a regression analysis using the glm()
function and assign the result to price_glm
. Fill in the XXXs in the code below.
price_glm <- glm(formula = XXX ~ XXX + XXX + XXX + XXX + XXX + XXX,
data = XXX)
price_glm <- glm(formula = price ~ bedrooms + bathrooms + living_sqm + waterfront + lat + long,
data = kc_house)
- Print your
price_glm
object. What do you see?
price_glm
Call: glm(formula = price ~ bedrooms + bathrooms + living_sqm + waterfront +
lat + long, data = kc_house)
Coefficients:
(Intercept) bedrooms bathrooms living_sqm waterfront
-65680578 -46146 17422 3157 790264
lat long
675209 -275008
Degrees of Freedom: 21612 Total (i.e. Null); 21606 Residual
Null Deviance: 2.91e+15
Residual Deviance: 1.09e+15 AIC: 594000
- Look at the names of your
price_glm
object with names()
.
names(price_glm)
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "model"
[22] "call" "formula" "terms"
[25] "data" "offset" "control"
[28] "method" "contrasts" "xlevels"
- Using
$
print the coefficients from your analysis.
price_glm$coefficients
(Intercept) bedrooms bathrooms living_sqm waterfront lat
-65680578 -46146 17422 3157 790264 675209
long
-275008
- Using the
summary()
function, show summary results from your price_glm
object.
summary(price_glm)
Call:
glm(formula = price ~ bedrooms + bathrooms + living_sqm + waterfront +
lat + long, data = kc_house)
Deviance Residuals:
Min 1Q Median 3Q Max
-1563708 -116127 -14138 84523 4180382
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.57e+07 1.41e+06 -46.51 < 2e-16 ***
bedrooms -4.61e+04 2.05e+03 -22.53 < 2e-16 ***
bathrooms 1.74e+04 3.07e+03 5.67 1.4e-08 ***
living_sqm 3.16e+03 2.95e+01 107.17 < 2e-16 ***
waterfront 7.90e+05 1.79e+04 44.14 < 2e-16 ***
lat 6.75e+05 1.12e+04 60.20 < 2e-16 ***
long -2.75e+05 1.14e+04 -24.15 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 5.06e+10)
Null deviance: 2.9129e+15 on 21612 degrees of freedom
Residual deviance: 1.0943e+15 on 21606 degrees of freedom
AIC: 594064
Number of Fisher Scoring iterations: 2
- The
$residuals
element in your price_glm
object contains the residuals (difference from the predicted and true dependent variable values). Add this vector as a new column called residuals
to your kc_house
object using mutate()
kc_house <- kc_house %>%
mutate(residuals = price_glm$residuals)
- Using the following template, create a histogram of the residuals.
ggplot(data = kc_house,
aes(x = residuals)) +
geom_histogram(col = "white") +
labs(title = "Residual regression")
ggplot(data = kc_house,
aes(x = residuals)) +
geom_histogram(col = "white") +
labs(title = "Residual regression")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
![](data:image/png;base64,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)
- What is the mean of the residuals? How do you interpret this?
kc_house %>%
summarise(resid_mean = mean(residuals))
# A tibble: 1 x 1
resid_mean
<dbl>
1 -0.000000151
- Now, create a new column called
residuals_abs
which shows the absolute value of the residuals (hint: use the abs()
function).
kc_house <- kc_house %>%
mutate(residuals_abs = abs(price_glm$residuals))
- Create a histogram of the absolute value of the residuals.
ggplot(data = kc_house,
aes(x = residuals_abs)) +
geom_histogram(col = "white") +
labs(title = "Residual regression")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
![](data:image/png;base64,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)
- What is the mean of the absolute value of the residuals? How do you interpret this? In general, do you think you can predict the price of a house very well based on the features in your regression model?
kc_house %>%
summarise(resid_abs_mean = mean(residuals_abs))
# A tibble: 1 x 1
resid_abs_mean
<dbl>
1 144018.
# On average, the model's fitted values are off by 144,018. So the model isn't terribly accurate on average.
X - Challenges
- Which zipcode has the highest percentage of houses on the waterfront? (Hint: group by zipcode, calculate the percentage of houses on the waterfront using
mean()
, then sort the data in descending order) with arrange()
, then select the first row with slice()
. Once you find it, try searching for that zipcode on Google Maps and see if it’s location makes sense!
kc_house %>%
group_by(zipcode) %>%
summarise(waterfront_p = mean(waterfront)) %>%
arrange(desc(waterfront_p)) %>%
slice(1)
# A tibble: 1 x 2
zipcode waterfront_p
<int> <dbl>
1 98070 0.203
- Which house had the highest price to living space ratio? To answer this, create a new variable called
price_to_living
that takes price / living_sqm
. Then, sort the data in descending order of this variable, and select the first row with slice()
! What id value do you get?
kc_house %>%
mutate(price_to_living = price / living_sqm) %>%
arrange(desc(price_to_living)) %>%
slice(1)
# A tibble: 1 x 29
id date price bedrooms bathrooms living_sqft lot_sqft
<chr> <dttm> <dbl> <int> <dbl> <int> <int>
1 6021… 2015-04-07 00:00:00 874950 2 1 1080 4000
# ... with 22 more variables: floors <dbl>, waterfront <int>, view <int>,
# condition <int>, grade <int>, above_sqft <int>, basement_sqft <int>,
# built_yr <int>, renovated_yr <int>, zipcode <int>, lat <dbl>,
# long <dbl>, sqft_living15 <int>, sqft_lot15 <int>, living_sqm <dbl>,
# lot_sqm <dbl>, above_sqm <dbl>, basement_sqm <dbl>, mansion <chr>,
# residuals <dbl>, residuals_abs <dbl>, price_to_living <dbl>
- Which are the top 10 zip codes in terms of mean housing prices? To answer this, group the data by zipcode, calculate the mean price, arrange the dataset in descending order of mean price, then select the top 10 rows!
kc_house %>%
group_by(zipcode) %>%
summarise(price_mean = mean(price)) %>%
arrange(desc(price_mean)) %>%
slice(1:10)
# A tibble: 10 x 2
zipcode price_mean
<int> <dbl>
1 98039 2160607.
2 98004 1355927.
3 98040 1194230.
4 98112 1095499.
5 98102 901258.
6 98109 879624.
7 98105 862825.
8 98006 859685.
9 98119 849448.
10 98005 810165.
- Create the following dataframe exactly as it appears.
1990 |
320 |
563966 |
3640900 |
234 |
1991 |
224 |
630441 |
5300000 |
244 |
1992 |
198 |
548169 |
2480000 |
223 |
1993 |
202 |
556612 |
3120000 |
226 |
1994 |
249 |
486834 |
2880500 |
209 |
1995 |
169 |
577771 |
3200000 |
224 |
1996 |
195 |
639534 |
3100000 |
240 |
1997 |
177 |
606058 |
3800000 |
234 |
1998 |
239 |
594159 |
1960000 |
241 |
kc_house %>%
filter(built_yr >= 1990 & built_yr < 1999) %>%
group_by(built_yr) %>%
summarise(N = n(),
price_mean = mean(price),
price_max = max(price),
living_sqm_mean = mean(living_sqm)) %>%
knitr::kable(digits = 0)
1990 |
320 |
563966 |
3640900 |
234 |
1991 |
224 |
630441 |
5300000 |
244 |
1992 |
198 |
548169 |
2480000 |
223 |
1993 |
202 |
556612 |
3120000 |
226 |
1994 |
249 |
486834 |
2880500 |
209 |
1995 |
169 |
577771 |
3200000 |
224 |
1996 |
195 |
639534 |
3100000 |
240 |
1997 |
177 |
606058 |
3800000 |
234 |
1998 |
239 |
594159 |
1960000 |
241 |
- Create a regression object called
living_glm
predicting the amount of living space (living_sqm
) as a function of bedrooms
, bathrooms
, waterfront
, and built_yr
. Explore the object with names()
and summary()
. Which variables seem to predict living space?
living_glm <- glm(formula = living_sqm ~ bedrooms + bathrooms + waterfront + built_yr,
data = kc_house)
names(living_glm)
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "model"
[22] "call" "formula" "terms"
[25] "data" "offset" "control"
[28] "method" "contrasts" "xlevels"
summary(living_glm)
Call:
glm(formula = living_sqm ~ bedrooms + bathrooms + waterfront +
built_yr, data = kc_house)
Deviance Residuals:
Min 1Q Median 3Q Max
-705.7 -32.0 -4.9 25.7 566.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 219.6403 27.7140 7.93 2.4e-15 ***
bedrooms 23.1621 0.4532 51.11 < 2e-16 ***
bathrooms 71.3214 0.6294 113.31 < 2e-16 ***
waterfront 62.5121 4.1476 15.07 < 2e-16 ***
built_yr -0.1297 0.0143 -9.09 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 2750)
Null deviance: 157675161 on 21612 degrees of freedom
Residual deviance: 59420739 on 21608 degrees of freedom
AIC: 232503
Number of Fisher Scoring iterations: 2
- The
chisq.test()
function allows you to do conduct a chi square test testing the relationship between two nominal variables. Look at the help menu to see how the function works. Then, conduct a chi-square test to see if there is a relationship between whether a house is on the waterfront and the grade of the house. Do houses on the waterfront tend to have higher (or lower) grades than houses not on the waterfront?
# First look at a table
table(kc_house$waterfront, kc_house$grade)
1 3 4 5 6 7 8 9 10 11 12 13
0 1 3 29 238 2026 8958 6028 2590 1106 379 79 13
1 0 0 0 4 12 23 40 25 28 20 11 0
chisq.test(table(kc_house$waterfront, kc_house$grade))
Pearson's Chi-squared test
data: table(kc_house$waterfront, kc_house$grade)
X-squared = 300, df = 10, p-value <2e-16