Consider the following Data:
Year |
Tea |
Coffee |
---|---|---|
1994 |
42.4 |
95.85 |
1995 |
42.12 |
97.28 |
1996 |
47.61 |
87.62 |
1997 |
60.86 |
92.04 |
1998 |
55.58 |
99.21 |
1999 |
50.61 |
95.63 |
2000 |
49.89 |
97.42 |
2001 |
56.77 |
93.93 |
2002 |
62.53 |
95.67 |
2003 |
68.31 |
99.25 |
2004 |
69.88 |
101.31 |
2005 |
72.99 |
101.68 |
2006 |
71.36 |
104.02 |
2007 |
90.78 |
106.09 |
2008 |
74.7 |
105.8 |
2009 |
67.15 |
102.15 |
2010 |
67.03 |
101.15 |
2011 |
87.83 |
104.05 |
2012 |
93.4 |
102.7 |
2013 |
78.9 |
105.28 |
2014 |
111.32 |
106.3 |
2015 |
98.39 |
104.96 |
2016 |
105.25 |
103.57 |
By using the definition and discussing what is relevant to the situation, interpret each of the following for both the coffee and tea data. Also, compare each for coffee and tea. Be sure to include the relevant information (state the value of or, in the case of the distribution, include the graphs) with each component.
Here I attach the R code with output
tea=c(42.4,42.12,47.61,60.86,55.58,50.61,49.89,56.77,62.53,68.31,69.88,72.99,71.36,90.78,74.7,67.15,67.03,87.83,93.4,78.9,111.32,98.39,105.25)
coffee=c(95.85,97.28,87.62,92.04,99.21,95.63,97.42,93.93,95.67,99.27,101.31,101.68,104.02,106.09,105.8,102.15,101.15,104.05,102.7,105.28,106.3,104.96,103.57)
year=c(1994:2016)
mean(tea)
median(tea)
mfv1(tea)
range(tea)
mean(coffee)
median(coffee)
mfv1(coffee)
summary(tea)
summary(coffee)
IQRtea=83.36-56.18=27.18
IQRcoffee=104.00-96.56=7.44
sd(tea)
sd(coffee)
range(coffee)
hist(coffee)
hist(tea)
boxplot(coffee)
boxplot(tea)
plot(coffee,tea)
plot(year,coffee)
plot(year,tea)
m=lm(coffee~tea)
summary(m)
cor(coffee,tea)
The measure of central tendency are mean, median , mode(mfv-most frequent observation) etc..
Measure of spread is range
range(tea)=111.32-42.12=69.2
range(coffee)=106.30-87.62=18.68
Here the model interval length is 5 units.
Here the model interval length is taken as 10 units.
Boxplot of coffee
From the plot it is clear that the distribution is negatively skewed and there is no outliers in the data.
Boxplot of tea
From the plot we can infer that the distribution is positively skewed and there is no outliers in the model.
The correlation between Coffee and tea is 0.769, which implies there is positive linear relatioship exist between the variable tea and coffee.
The obtained model is:
coffee=86.3200+0.1954*tea
Consider the following Data: Year Tea (L per person) Coffee (L per person) 1994 42.4 95.85...
Find out the Mean, Median ,Modal Interval, Range, IQR ,Standard Deviation, of this set of data Year Tea (L per person) Coffee(L per person) 1994 42.4 95.85 42.12 1995 97.28 1996 47.61 87.62 1997 60.86 92.04 1998 55.58 99.21 50.61 95.63 1999 2000 49.89 97.42 56.77 2001 93.93 62.53 2002 95.67 2003 68.31 99.25 2004 101.31 69.88 2005 72.99 101.68 104.02 2006 71.36 106.09 2007 90.78 105.8 2008 74.7 2009 67.15 102.15 2010 67.03 101.15 87.83 2011 104.05 102.7 2012...