Calculate the descriptive statistics from the data and display in a table. Be su

Question

Calculate the descriptive statistics from the data and display in a table. Be sure to comment on the central tendency, variability and shape for all of the variables excluding Year, Name and Model. Include information regarding the quartiles for Price, Kilometers and PowerKW. How would you interpret the mean of dummy variables such as Automatic or Petrol?

1295647474 Mean 12.66639561 Mean 128958.9514 Mcan 0.632983794 Mean 0.052430887 Mean 133.593899 Mean 0.014299333 Mean 0.771210577 Mcan 0.097235462 Mcan Standard Erro 3775707406 Standerd Erro 0.193277381 Standard Erro 0015430834 Standard Erro 1078 258984 Standard rro0014888751 Standard Erro 0.006885229 Standard Erro1428941164 Standard Erro 0.003067328 Standard Erro 0.01297549 Standerd Erro 0.003152062 Standard Erro 0009938067 8820 Median 16170 Mode 8 86289 andard 150000 Medien 50000 Mode 129 Median 129 Miode d Devl 6.258919903 Standard Devi 2220889 Standard Dav 19 Standard Da0.1 d Devi 0.321876792 Sample Varian 149545087.7 Sample Varian 391865972 Sample Varan 0.249778051 Sample Variar 1219511927 Sample Variar 0232536985 Sample Varia: 0.049729295 Sampie Varian 2141.924619 Sample Varian 0.014108311 Sample Varian 0.178513132 Sample Varian 0.08786447 ample Varan 0.103604669 3.884537665 2.38275396 d DN- 0.223000662 Standard Dev. 46.2 d Dexl 0.420253652 Standard Devi a 1493156913 rtosis 14.20137333 Kurtosis 1.37449627 Kurtoss 5.423553151 Kurtosis Skewness 2.008317796 Skewness 0.532509992 Skewness 008599807 Skewness-158391682 Skewness 55260079 Skewness 4.021728595 Skewness 1457234332 Skewness 8.193887348 Skewness 1.29316544 Skewnss2.722722231 Skewness 86730 Rage 45000 Range Range Range 5000 Minimum 13591342 Sum 3296 Sum 1019 Count 55 Sum 1049 Count 09 Sum 1019 Count 102 Sum 1019 Count 13521s000 Sum 1019 Count 1049 Count 1049 Count 049 Count 1049 Count

Explanation / Answer

1)Price

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The price variable has got 1049 observations.

Mean: We see the Mean of the price variable is 12956 approx.

Median: Median gives us the value which divides the data into two halves. Here we see median price value is 8820 above & below which lies 50% of the prices.

Mode: Mode is the most frequently observation. In our case, the most frequently price is 16170.

Minimum: We see the minimum price is 735.

Maximum: Also we see the maximum price 87465.

Standard Deviation: This is the standard deviation of the variable. This gives information regarding the spread of the distribution of the variable. In these results, the Standard deviation for price is 12228.86. We see here, some of the observations are spread outside 3 standard deviations on each side of the mean.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for price is 2.008, which suggests price is positively skewed.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 5.91, which means price has Leptokurtic distribution.

2)Age

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The age variable has got 1049 observations.

Mean: We see the Mean of the age variable is 12.66 approx.

Median: Median gives us the value which divides the data into two halves. Here we see median age value is 12 above & below which lies 50% of the ages.

Mode: Mode is the most frequently observation. In our case, the most frequently age is 6.

Minimum: We see the minimum age is 1.

Maximum: Also we see the maximum age 42.

Standard Deviation: This is the standard deviation of the variable. This gives information regarding the spread of the distribution of the variable. In these results, the Standard deviation for age is 6.26. We see here, most of the observations are spread within 3 standard deviations on each side of the mean.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for age is 0.53, which suggests age is very close to symmetry.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 0.10, which means age has almost Mesokurtic distribution.

3)Automatic

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The automatic variable has got 1049 observations.

Mean: Since Automatic is a dummy variable, the interpretation of mean will be little different, if we see the Mean greater than 0.5 we can suggest that most of cars are automatic or if Mean is less than 0.5 we can suggest that most of cars are non-automatic. In our case, we have got mean as 0.52 which suggests that there are almost equal numbers of the cars for both variants.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for automatic is -0.08, which suggests automatic is very close to symmetry.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as -1.9, which means automatic has Platykurtic distribution.

4)Kilometer

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The kilometer variable has got 1049 observations.

Mean: We see the Mean of the kilometer variable is 128899 approx.

Median: Median gives us the value which divides the data into two halves. Here we see median kilometer value is 150000 above & below which lies 50% of the kilometers.

Mode: Mode is the most frequently observation. In our case, the most frequently kilometer is 150000.

Minimum: We see the minimum kilometer is 5000.

Maximum: Also we see the maximum kilometer 150000.

Standard Deviation: This is the standard deviation of the variable. This gives information regarding the spread of the distribution of the variable. In these results, the Standard deviation for kilometer is 34922.94. We see here, most of the observations are spread within 3 standard deviations on each side of the mean.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for kilometer is -1.58, which suggests kilometer is negatively skewed.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 1.49, which means kilometer has almost Platykurtic distribution.

5)Patrol

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The patrol variable has got 1049 observations.

Mean: Since Patrol is a dummy variable, the interpretation of mean will be little different, if we see the Mean greater than 0.5 we can suggest that most of cars are patrol or if Mean is less than 0.5 we can suggest that most of cars are non-patrol. In our case, we have got mean as 0.63 which suggests that most of the cars have patrol engine.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for patrol is -0.55, which suggests patrol is very close to symmetry.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as -1.69, which means patrol has Platykurtic distribution.

6)Damage

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The damage variable has got 1049 observations.

Mean: Since Damage is a dummy variable, the interpretation of mean will be little different, if we see the Mean greater than 0.5 we can suggest that most of cars are damage or if Mean is less than 0.5 we can suggest that most of cars are non-damage. In our case, we have got mean as 0.05 which suggests that almost all of the cars have no damage.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for damage is 4.02, which suggests damage is skewed to the right.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 14.2, which means damage has Leptokurtic distribution.

7)PowerKW

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The powerKW variable has got 1049 observations.

Mean: We see the Mean of the powerKW variable is 133.59 approx.

Median: Median gives us the value which divides the data into two halves. Here we see median powerKW value is 129 above & below which lies 50% of the powerKW.

Mode: Mode is the most frequently observation. In our case, the most frequently powerKW is 129.

Minimum: We see the minimum powerKW is 55.

Maximum: Also we see the maximum powerKW 426.

Standard Deviation: This is the standard deviation of the variable. This gives information regarding the spread of the distribution of the variable. In these results, the Standard deviation for powerKW is 46.28. We see here, most of the observations are spread within 3 standard deviations on each side of the mean.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for powerKW is 1.45, which suggests powerKW is positively skewed.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 4.37, which means powerKW has Leptokurtic distribution.

8)Hatchback

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The hatchback variable has got 1049 observations.

Mean: Since Hatchback is a dummy variable, the interpretation of mean will be little different, if we see the Mean greater than 0.5 we can suggest that most of cars are hatchback or if Mean is less than 0.5 we can suggest that most of cars are non-hatchback. In our case, we have got mean as 0.014 which suggests that almost all of the cars are non-hatchback.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for hatchback is 8.19, which suggests hatchback is skewed to the right.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 65.26, which means hatchback has Leptokurtic distribution.

9)Sedan

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The sedan variable has got 1049 observations.

Mean: Since Sedan is a dummy variable, the interpretation of mean will be little different, if we see the Mean greater than 0.5 we can suggest that most of cars are sedan or if Mean is less than 0.5 we can suggest that most of cars are non-sedan. In our case, we have got mean as 0.77 which suggests that almost all of the cars are sedan.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for sedan is -1.29, which suggests sedan is skewed to the left.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as -0.33, which means sedan has Platykurtic distribution.

10)Convertible

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The convertible variable has got 1049 observations.

Mean: Since Convertible is a dummy variable, the interpretation of mean will be little different, if we see the Mean greater than 0.5 we can suggest that most of cars are convertible or if Mean is less than 0.5 we can suggest that most of cars are non-convertible. In our case, we have got mean as 0.09 which suggests that almost all of the cars are non-convertible.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for convertible is 2.72, which suggests convertible is skewed to the right.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 5.42, which means convertible has Leptokurtic distribution.

11)Coupe

Analysis:

        Observations: This metrics tells us the number of observations (or cases) that were valid (i.e.,       not missing) for that variable. The coupe variable has got 1049 observations.

Mean: Since Coupe is a dummy variable, the interpretation of mean will be little different, if we see the Mean greater than 0.5 we can suggest that most of cars are coupe or if Mean is less than 0.5 we can suggest that most of cars are non-coupe. In our case, we have got mean as 0.11 which suggests that most of the cars are non-coupe.

Skewness: Skewness measures the degree and direction of asymmetry. A symmetric distribution such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left, when the value of skewness is less than 0 and has a negative skewness. Also a distribution that is skewed to the right, when the value of skewness is greater than 0 and has a positive skewness. In these results, we see the skewness for coupe is 2.38, which suggests coupe is skewed to the right.

Kurtosis: Kurtosis is a measure of the heaviness of the tails of a distribution. A normal distribution has a kurtosis of 3 (Mesokurtic). Heavy tailed distributions (Leptokurtic) will have kurtosis greater than 3 and light tailed distributions (Platykurtic) will have kurtosis less than 3. In our case, we see the value of kurtosis as 3.68, which means coupe has almost Mesokurtic distribution.

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