Improving the Regression Model for ICU Hours
Added on 20191001
15 Pages3286 Words326 Views



Table of Contents
Introduction.................................................................................................................... 2
Research Objective........................................................................................................... 2
Data Description.............................................................................................................. 2
Analysis........................................................................................................................ 4
Conclusion................................................................................................................... 12
Recommendation........................................................................................................... 13
References................................................................................................................... 14
Introduction.................................................................................................................... 2
Research Objective........................................................................................................... 2
Data Description.............................................................................................................. 2
Analysis........................................................................................................................ 4
Conclusion................................................................................................................... 12
Recommendation........................................................................................................... 13
References................................................................................................................... 14
Introduction
The UTS Hospital wants to analyze his data to know the relationship between ICU hours,
length of stay, marital status, and gender. The technique of regression analysis is applied to
predict the ICU hours with the help of length of stay, married, single, and others. The technique
of hypothesis testing is used to test if there is any difference in the mean length of stay between
the two type of gender in the UTS hospital. Portela, F., (2014).
Research Objective
The research objective is to analyze the relationship of ICU hours in the UTS hospital
with the length of stay (LOC) and marital status. The dependent variable is ICU hours. The
independent variables are the length of stay and marital status in the UTS hospital. Among
various categories of marital status, the area of concern is to analyze the ICU hours for married,
single, and others. Since most of the patients in the hospital belong to the married or single
category, hence the emphasis is done on these categories. McCance, K. L., & Huether, S. E.
(2018).
I also want to test if there is a difference in the average length of stay at the UTS Hospital
between males and females. The dependent variable is the length of stay. The independent
variable is gender which is classified as either male or female.
Data Description
The variable ICU hours is a continuous variable which is measured by the ratio scale of
measurement. The ICU hour for patient 1 in comparison with that of patient 2. The variable
The UTS Hospital wants to analyze his data to know the relationship between ICU hours,
length of stay, marital status, and gender. The technique of regression analysis is applied to
predict the ICU hours with the help of length of stay, married, single, and others. The technique
of hypothesis testing is used to test if there is any difference in the mean length of stay between
the two type of gender in the UTS hospital. Portela, F., (2014).
Research Objective
The research objective is to analyze the relationship of ICU hours in the UTS hospital
with the length of stay (LOC) and marital status. The dependent variable is ICU hours. The
independent variables are the length of stay and marital status in the UTS hospital. Among
various categories of marital status, the area of concern is to analyze the ICU hours for married,
single, and others. Since most of the patients in the hospital belong to the married or single
category, hence the emphasis is done on these categories. McCance, K. L., & Huether, S. E.
(2018).
I also want to test if there is a difference in the average length of stay at the UTS Hospital
between males and females. The dependent variable is the length of stay. The independent
variable is gender which is classified as either male or female.
Data Description
The variable ICU hours is a continuous variable which is measured by the ratio scale of
measurement. The ICU hour for patient 1 in comparison with that of patient 2. The variable
Length of stay is also a continuous variable which is measured by the ratio scale of
measurement. Marital status is measured by the nominal scale of measurement as it is has six
categories. The various categories of marital status are divorced, married, separated, single,
unknown, and widowed. The variable gender is a discrete variable which is measured by the
nominal scale of measurement. Gender is classified as either male or female. Chatfield, C.
(2018).
The table of descriptive statistics for the variables measured by the ratio scale of
measurement are given below.
The average length of stay is 4.2 units with a standard deviation of 7.9. The average ICU
hours is 8.3 hours with a standard deviation of 77.59. The large value of the standard deviation (a
measure of dispersion) is an indication that the value of average length of stay is not
reliable. With the large value of standard deviation (which is a measure of dispersion), I can say
measurement. Marital status is measured by the nominal scale of measurement as it is has six
categories. The various categories of marital status are divorced, married, separated, single,
unknown, and widowed. The variable gender is a discrete variable which is measured by the
nominal scale of measurement. Gender is classified as either male or female. Chatfield, C.
(2018).
The table of descriptive statistics for the variables measured by the ratio scale of
measurement are given below.
The average length of stay is 4.2 units with a standard deviation of 7.9. The average ICU
hours is 8.3 hours with a standard deviation of 77.59. The large value of the standard deviation (a
measure of dispersion) is an indication that the value of average length of stay is not
reliable. With the large value of standard deviation (which is a measure of dispersion), I can say
that the average is not a preferred measure of the Central tendency for the variable namely length
of stay and ICU hours. Holcomb, Z. C. (2016).
The value of skewness for the length of stay and ICU hours is 7.04 and 16.88
respectively. Hence the distribution of length of stay, as well as ICU hours, is skewed to the
right. The positive skewness shows that there are very few patients with a large value of the
length of stay. It is also evident that there are very few patients in the UTS hospital who have
long ICU hours.
Median is the preferred measure of central tendency when the distribution of data is
skewed to left or right. For data which is observed to be positively or negatively skewed, the
interquartile range is considered as the preferred measure of scatterness. The median value for
the length of stay in UTS hospital is 2 days. The median value of ICU hours in the UTS hospital
is zero. Hinton, P. R. (2014).
Mode is the preferred measured of central tendency for the variables which are measured
by the nominal scale of measurement or are categorical in nature. There is no method to measure
the dispersion for these categorical variables. The mode is defined the number which occurs a
maximum times in the data. The value of mode for gender and marital status is given below.
Boudreau, N. S. (2016).
Gender Marital Status
Mode Female Married
In the given data, there are mostly females patients. The marital status for most of the
patients is married.
of stay and ICU hours. Holcomb, Z. C. (2016).
The value of skewness for the length of stay and ICU hours is 7.04 and 16.88
respectively. Hence the distribution of length of stay, as well as ICU hours, is skewed to the
right. The positive skewness shows that there are very few patients with a large value of the
length of stay. It is also evident that there are very few patients in the UTS hospital who have
long ICU hours.
Median is the preferred measure of central tendency when the distribution of data is
skewed to left or right. For data which is observed to be positively or negatively skewed, the
interquartile range is considered as the preferred measure of scatterness. The median value for
the length of stay in UTS hospital is 2 days. The median value of ICU hours in the UTS hospital
is zero. Hinton, P. R. (2014).
Mode is the preferred measured of central tendency for the variables which are measured
by the nominal scale of measurement or are categorical in nature. There is no method to measure
the dispersion for these categorical variables. The mode is defined the number which occurs a
maximum times in the data. The value of mode for gender and marital status is given below.
Boudreau, N. S. (2016).
Gender Marital Status
Mode Female Married
In the given data, there are mostly females patients. The marital status for most of the
patients is married.
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