Question

In: Statistics and Probability

Open Hurricanes data. Test if there is a significant difference in the death by Hurricanes and...

Open Hurricanes data.

Test if there is a significant difference in the death by Hurricanes and Min Pressure measured. Answer the questions for Assessment. (Pick the closest answer)

7. What is the P-value?

  • a. #DIV/0!
  • b. 0.384808843
  • c. 0.634755682
  • d. None of these

8. What is the Statistical interpretation?

  • a. The P-value is too large to have a conclusive answer.
  • b. The P-value is too small to have a conclusive answer.
  • c. ​​The P-value is much smaller than 5% thus we are certain that the average of hurricane deaths is significantly different from average min pressure.
  • d. None of the above.

9. What is the conclusion?

  • a. The statistics does not agree with the intuition since one would expect that stronger hurricanes to be deadlier.
  • b. ​​Statistical interpretation agrees with the intuition, the lower the pressure the stronger the hurricanes.
  • c. Statistics confirms that hurricanes’ pressure does relate to the death count.
  • d. The test does not make statistical sense, it compares “apples and oranges”.

Year   Name   MinPressure_before   Gender_MF   Category   alldeaths
1950   Easy   958   1   3   2
1950   King   955   0   3   4
1952   Able   985   0   1   3
1953   Barbara   987   1   1   1
1953   Florence   985   1   1   0
1954   Carol   960   1   3   60
1954   Edna   954   1   3   20
1954   Hazel   938   1   4   20
1955   Connie   962   1   3   0
1955   Diane   987   1   1   200
1955   Ione   960   0   3   7
1956   Flossy   975   1   2   15
1958   Helene   946   1   3   1
1959   Debra   984   1   1   0
1959   Gracie   950   1   3   22
1960   Donna   930   1   4   50
1960   Ethel   981   1   1   0
1961   Carla   931   1   4   46
1963   Cindy   996   1   1   3
1964   Cleo   968   1   2   3
1964   Dora   966   1   2   5
1964   Hilda   950   1   3   37
1964   Isbell   974   1   2   3
1965   Betsy   948   1   3   75
1966   Alma   982   1   2   6
1966   Inez   983   1   1   3
1967   Beulah   950   1   3   15
1968   Gladys   977   1   2   3
1969   Camille   909   1   5   256
1970   Celia   945   1   3   22
1971   Edith   978   1   2   0
1971   Fern   979   1   1   2
1971   Ginger   995   1   1   0
1972   Agnes   980   1   1   117
1974   Carmen   952   1   3   1
1975   Eloise   955   1   3   21
1976   Belle   980   1   1   5
1977   Babe   995   1   1   0
1979   Bob   986   0   1   1
1979   David   970   0   2   15
1979   Frederic   946   0   3   5
1980   Allen   945   0   3   2
1983   Alicia   962   1   3   21
1984   Diana   949   1   2   3
1985   Bob   1002   0   1   0
1985   Danny   987   0   1   1
1985   Elena   959   1   3   4
1985   Gloria   942   1   3   8
1985   Juan   971   0   1   12
1985   Kate   967   1   2   5
1986   Bonnie   990   1   1   3
1986   Charley   990   0   1   5
1987   Floyd   993   0   1   0
1988   Florence   984   1   1   1
1989   Chantal   986   1   1   13
1989   Hugo   934   0   4   21
1989   Jerry   983   0   1   3
1991   Bob   962   0   2   15
1992   Andrew   922   0   5   62
1993   Emily   960   1   3   3
1995   Erin   973   1   2   6
1995   Opal   942   1   3   9
1996   Bertha   974   1   2   8
1996   Fran   954   1   3   26
1997   Danny   984   0   1   10
1998   Bonnie   964   1   2   3
1998   Earl   987   0   1   3
1998   Georges   964   0   2   1
1999   Bret   951   0   3   0
1999   Floyd   956   0   2   56
1999   Irene   987   1   1   8
2002   Lili   963   1   1   2
2003   Claudette   979   1   1   3
2003   Isabel   957   1   2   51
2004   Alex   972   0   1   1
2004   Charley   941   0   4   10
2004   Frances   960   1   2   7
2004   Gaston   985   0   1   8
2004   Ivan   946   0   3   25
2004   Jeanne   950   1   3   5
2005   Cindy   991   1   1   1
2005   Dennis   946   0   3   15
2005   Ophelia   982   1   1   1
2005   Rita   937   1   3   62
2005   Wilma   950   1   3   5
2005   Katrina   902   1   3   1833
2007   Humberto   985   0   1   1
2008   Dolly   963   1   1   1
2008   Gustav   951   0   2   52
2008   Ike   935   0   2   84
2011   Irene   952   1   1   41
2012   Isaac   965   0   1   5
2012   Sandy   945   1   2   159
                  

Solutions

Expert Solution

Solution-7:

Ho mu1=mu2

Ha;mu1 not =mu2

alpha=0.05

use t.test function in R to get t and p value

Rcode:

df=read.table(header = TRUE, text ="
Year Name MinPressure_before Gender_MF Category alldeaths
1950 Easy 958 1 3 2
1950 King 955 0 3 4
1952 Able 985 0 1 3
1953 Barbara 987 1 1 1
1953 Florence 985 1 1 0
1954 Carol 960 1 3 60
1954 Edna 954 1 3 20
1954 Hazel 938 1 4 20
1955 Connie 962 1 3 0
1955 Diane 987 1 1 200
1955 Ione 960 0 3 7
1956 Flossy 975 1 2 15
1958 Helene 946 1 3 1
1959 Debra 984 1 1 0
1959 Gracie 950 1 3 22
1960 Donna 930 1 4 50
1960 Ethel 981 1 1 0
1961 Carla 931 1 4 46
1963 Cindy 996 1 1 3
1964 Cleo 968 1 2 3
1964 Dora 966 1 2 5
1964 Hilda 950 1 3 37
1964 Isbell 974 1 2 3
1965 Betsy 948 1 3 75
1966 Alma 982 1 2 6
1966 Inez 983 1 1 3
1967 Beulah 950 1 3 15
1968 Gladys 977 1 2 3
1969 Camille 909 1 5 256
1970 Celia 945 1 3 22
1971 Edith 978 1 2 0
1971 Fern 979 1 1 2
1971 Ginger 995 1 1 0
1972 Agnes 980 1 1 117
1974 Carmen 952 1 3 1
1975 Eloise 955 1 3 21
1976 Belle 980 1 1 5
1977 Babe 995 1 1 0
1979 Bob 986 0 1 1
1979 David 970 0 2 15
1979 Frederic 946 0 3 5
1980 Allen 945 0 3 2
1983 Alicia 962 1 3 21
1984 Diana 949 1 2 3
1985 Bob 1002 0 1 0
1985 Danny 987 0 1 1
1985 Elena 959 1 3 4
1985 Gloria 942 1 3 8
1985 Juan 971 0 1 12
1985 Kate 967 1 2 5
1986 Bonnie 990 1 1 3
1986 Charley 990 0 1 5
1987 Floyd 993 0 1 0
1988 Florence 984 1 1 1
1989 Chantal 986 1 1 13
1989 Hugo 934 0 4 21
1989 Jerry 983 0 1 3
1991 Bob 962 0 2 15
1992 Andrew 922 0 5 62
1993 Emily 960 1 3 3
1995 Erin 973 1 2 6
1995 Opal 942 1 3 9
1996 Bertha 974 1 2 8
1996 Fran 954 1 3 26
1997 Danny 984 0 1 10
1998 Bonnie 964 1 2 3
1998 Earl 987 0 1 3
1998 Georges 964 0 2 1
1999 Bret 951 0 3 0
1999 Floyd 956 0 2 56
1999 Irene 987 1 1 8
2002 Lili 963 1 1 2
2003 Claudette 979 1 1 3
2003 Isabel 957 1 2 51
2004 Alex 972 0 1 1
2004 Charley 941 0 4 10
2004 Frances 960 1 2 7
2004 Gaston 985 0 1 8
2004 Ivan 946 0 3 25
2004 Jeanne 950 1 3 5
2005 Cindy 991 1 1 1
2005 Dennis 946 0 3 15
2005 Ophelia 982 1 1 1
2005 Rita 937 1 3 62
2005 Wilma 950 1 3 5
2005 Katrina 902 1 3 1833
2007 Humberto 985 0 1 1
2008 Dolly 963 1 1 1
2008 Gustav 951 0 2 52
2008 Ike 935 0 2 84
2011 Irene 952 1 1 41
2012 Isaac 965 0 1 5
2012 Sandy 945 1 2 159


"
)
df

t.test(df$MinPressure_before ,df$alldeaths)

Output:

Welch Two Sample t-test

data: df$MinPressure_before and df$alldeaths
t = 46.089, df = 94.058, p-value < 0.00000000000000022
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
884.2762 963.8958
sample estimates:
mean of x mean of y
964.22581 40.13978

we got p-value < 0.00000000000000022

p=0.000

p<0.05

Reject Ho

d. None of these

Solution-8:

c. ​​The P-value is much smaller than 5% thus we are certain that the average of hurricane deaths is significantly different from average min pressure.

Solution-9:

c. Statistics confirms that hurricanes’ pressure does relate to the death count.


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