Investigating Customers Taste on a newly made Product & How much They are ready to Pay for that Product

Introduction: A snack food making company has gathered data from its customers about whether they like the product or not they make and if they like which version they prefer between version 1 and version 2, and also about the money they would like to spend on that product. The investigation on the customers’ preference and their willingness to spend on the product is based on a large hypothetical data set. Among this large population each of us (student) has to work with the separate sample, each consisting of 100 individual. The company gathers the data for following two hypothesis to be tested:

  1. Is the proportion of people that prefer version 2 significantly different to 50%?
  2. Is the mean of the variable “How much they would pay” significantly different to $2?

 

 

The problems of getting survey data in the real world: The data used in the assignment is not real world data. We have used hypothetical data to investigate our research questions. Because there are many problems of getting survey data in the real world. Survey data collection methods are time consuming and costly. Also lack of proper training period for the data collectors could create several problems like misunderstanding, misconception and biasness during the data collection process. That’s why sometimes we create hypothetical data through simulation instead of collecting real world data.

But if we have to collect the survey data we would be needing proper data collection methods, instruction form an experience person in that field and proper training period and guidance for the filed workers who would collect data from the respondents.

 

 

Description of Data set: There are 1000 samples of size 100 on the peoples’ preference on the product made by the company. Among them 60040 persons liked the product and 39960 persons didn’t.
We, each of the students are given a separate sample to analyze with. Each sample data set contains 100 observations on 6 variables- Gender, do they like the product, which version is the best, how much they would pay, are they old, income. Among these variables “how much they would pay” and “income” are the numerical variables and rest of them are categorical variables.

 

 

 

 

 

Summary of the data set: Numerical and graphical summary of the variables are given below through univariate and bivariate descriptive statistics, histograms and bar diagrams.

  1. i) “Income”:
    Descriptive statistics:
Minimum: 45000
Maximum: 80000
Range: 35000
Count: 100
Sum: 6217000
Mean: 62170
Median: 63000
Mode: 63000
Standard Deviation: 10200
Variance: 104100000
Quartiles: Quartiles:
Q1 –> 53000
Q2 –> 63000
Q3 –> 70000
Skewness: 0.05892
Kurtosis: 1.823

Descriptive statistics shows that average income of the 100 sample respondents is $62170 with standard deviation is equal to 10200. And among that $45000 is the lowest income and $80000 being the highest income.

 

 

 

 

 

 

 

Histogram:his

 

The histogram of the variable “Income” shows that the highest frequency occurs with the class $60000-$65000 that is, highest number of respondents have income in that class. And the distribution of the variable “Income” isn’t normally distributed.

  1. ii) “How much they would pay”:

    Descriptive statistics:

Minimum: 0
Maximum: 3.3
Range: 3.3
Count: 100
Sum: 249.1
Mean: 2.491
Median: 3.1
Mode: 3.3
Standard Deviation: 1.249
Variance: 1.561
Quartiles: Quartiles:
Q1 –> 0.5
Q2 –> 3.1
Q3 –> 3.2
Skewness: -1.273
Kurtosis: 2.676

Descriptive statistics shows that average amount of money people would like to spend on the product is $2.491 with standard deviation is equal to 1.249. And among the respondent someone would prefer to spend no money on the product (minimum is $0) and some people would like to spend maximum $3.3.

Histogram: his-2

The histogram of the variable “How much they would pay” shows that the highest frequency occurs with the class $3-$3.5 that is, highest number of respondents want to spend $3-3.5 on the product. Also it is noticeable that no respondent wants to spend between $0.5-2.5.  And the distribution of the variable “Income” isn’t normally distributed.

 

  1. Descriptive bivariate and graphical summary:
  2. “Gender” and “Do they like the product”:
sample collector id  

11600019
Count of do they like the product? Column Labels
Row Labels                        Like      Hate Grand   Total
female 26 14 40
male 35 25 60
Grand Total 61 39 100

 

tem

The bar diagram shows that 65% of the females like the product and 58% of the males like the product. That means, female respondents preferred the product than male respondents.

 

  1. “How much they would pay” and “Gender”:

Numerical summaries of the females among people who would pay:

Minimum: 0
Maximum: 3.3
Range: 3.3
Count: 40
Sum: 112.2
Mean: 2.805
Median: 3.1
Mode: 3.3
Standard Deviation: 0.9679
Variance: 0.9369
Quartiles: Quartiles:
Q1 –> 3
Q2 –> 3.1
Q3 –> 3.2
Skewness: 0.2
Kurtosis: 2.963

 

The numerical summaries shows that female are willing to spend on average $2.805 on that product.

 

 

Numerical summaries of the males among people who would pay:

Minimum: 0
Maximum: 3.3
Range: 3.3
Count: 60
Sum: 136.9
Mean: 2.282
Median: 3.1
Mode: 3.3
Standard Deviation: 1.374
Variance: 1.888
Quartiles: Quartiles:
Q1 –> 0.4
Q2 –> 3.1
Q3 –> 3.2
Skewness: 00.8663
Kurtosis: 1.781

 

 

The numerical summaries shows male are willing to spend on average $2.282 on that product. That mea

 

 

 

Back to back histogram to compare the amount male and female would pay for that product:his3

 

Back to back histogram indicates that female prefer to spend more money on the product than by the male.

Confidence interval for proportion and mean:

  1. 95% confidence interval for the proportion of people that prefer version 2 is given by:

P {p^ -Z S.E (p^)} = 1-0.05

 

Here, n=61;   Zα/2 =1.96; p^ =   ;    S.E (p^) =

= 0.62                     =  = 0.0621

So,

P {0.62-1.96*0.0621  p }= 0.95

=> P {0.50  p  } = 0.95

So, {0.50, 0.74} is the 95% confidence interval for the population proportion of people that prefer version 2.

  1. 95% confidence interval for the mean of the variable “How much they would pay” is given by:

P { – Z*S.E ()} =1-0.05
Here, n=61;   Zα/2 =1.96;   2.498360656; =  =  = 0.16004596

So,

P {2.498360656 – 1.96*0.16004596   } = 0.95

=> P {2.1847   } = 0.95

So, {2.1867, 2.8121} is the 95% confidence interval for the mean of the variable “How much they would pay”.

 

Hypothesis Testing:

  1. The null hypothesis is, P= 0.50
    against,
    alternative hypothesis, P≠ 0.50

The test statistic is given by,

Z=  ~   N (0, 1)

Here, n=61;     P=  =  = 0.6229

 

So,      Z=  = 1.9197

This is the calculated value of Z.

And the tabulated value of Z at 5% level of significance is obtained from the standardized normal distribution table as 1.96.

Since the calculated value doesn’t exceed the tabulated value we may conclude that the null hypothesis is accepted at 5% level of significance. Hence, the claim, proportion that prefer version 2 is different to 50% is not significant enough, i.e. isn’t true at 5% level of significance.

  1. The null hypothesis is, µ = $2
    against,
    alternative hypothesis, µ ≠ $2

The test statistic is given by,

Z=   ~ N (0, 1)
= = 3.1138

This is the calculated value of Z.

And the tabulated value of Z at 5% level of significance is obtained from the standardized normal distribution table as 1.96.

Since the calculated value exceeds the tabulated value we may conclude that the null hypothesis is rejected at 5% level of significance. That is, we may claim that the mean of the variable “How much they would pay” is different to $2 at 5% level of significance.

 

Disadvantages of Quantitative methods: Quantitative methods are rigid and provides less detail on the motivational, attitudes and behavioral study subjects. The findings of quantitative research methods are numerical and therefore lack a detailed narrative of human perception. The respondents may also provide answers that reflect their preconceived hypothesis.
In social and behavioral science research study qualitative research is more appropriate than the quantitative research, since qualitative research deals with the people’s opinion and behavioral type questions.

Conclusion: The concept of the assignment was to use a hypothetical data set to make inferences/decisions about a real world business scenario. In the previous sections of the assignment we have got familiar with the problem of getting survey data in the real world. We also have got familiar with the sampling distribution of an estimate. We have drawn inferences about a large population (100000) from a small sample of size 100. Numerical and graphical summary of the data was done using univariate and bivariate descriptive statistics as well as bar diagram, histogram and back to back histogram.
Inferences about the population parameters were also drawn using the confidence intervals. The research questions of the assignment was that a snack food making company wanted to know if the proportion of people that prefer version 2 of the product is significantly different to 50% and also if the mean of the variable “How much they would pay” is significantly different to $2. And simple hypothesis testing concludes that the first claim isn’t significantly true and the second claim is significantly true, i.e. version 2 isn’t significantly preferred over version 1 and people would pay significantly different than $2. We have also discussed the disadvantages of quantitative research in social and behavioral science and the use of qualitative research in that area.

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Descriptive analysis of the Quantitative variables

Results and Discussions:
Descriptive statistics of the Quantitative variables:

Quantitative Variables No. of Observation, N         Mean Standard Deviation
AGE 50 49.14 11.502
BMI 50 26.48 3.3383
LVEF 50    
QRS Score 50 6.88 3.842
Vessel Score 50 1.78 0.764
Friesinger Score 50 7.52 3.019

 

Frequency Distribution of Qualitative Variables:

SEX

                Frequency                Percent
               Male                 44                88
               Female                 6                12
               Total                 50                100

                                                          

                                                                           SMOKING

                Frequency                Percent
                No 17 34
                Yes 33 66
               Total 50 100

 

                                                                             DM

                Frequency                Percent
No 31 62
Yes 19 38
Total 50 100

 

                                                                             HTN

                Frequency                Percent
No 24 48
Yes 26 52
Total 50 100

                       

                                                                      DYSLIPIDEMIA

                Frequency                Percent
No 40 80
Yes 10 20
Total 50 100

                                                                 

                                                                        F/H of IHD

                Frequency                Percent
No 43 86
Yes 7 14
Total 50 100


Site of MI

                Frequency                Percent
Anterior 27 54
Inferior 23 46
Total 50 100

 

Relationship of Demographic Variables with QRS score:
The bivariate correlation analysis of Age with QRS score shows that (r=+0.075, p-value=0.606) , which implies that there is a very week positive relationship between those variables and the p-value suggests that the degree of relationship isn’t significant enough.
Again bivariate correlation analysis of BMI with QRS score shows that (r=+0.613, p-value=0.00), which implies that there is a very strong positive relationship between those variables and the p-value suggests that the degree of relationship is highly significant.

 

 

 

Relationship of different groups of SEX with QRS score (ANOVA):

  SEX  

P-Value

MALE FEMALE
QRS Score 7.30 ± 3.837 3.83 ± 2.317 0.037

 

There is significant difference in QRS score between Male and Female group of patients (F=4.603, P= 0.037).


ANOVA of QRS Score Among Different Groups of Cardiovascular Risk Factors:
#
Data are analyzed using ANOVA statistics and are presented as mean ± SD;

  SMOKING  

P-Value

NO YES
QRS Score 5.24 ± 3.345 7.73 ± 3.851 0.028(sig.)

 

There is significant difference in QRS score between Smoker and Non-smoker group of patients (F=5.117, P= 0.028).

  DM  

P-Value

NO YES
QRS Score 6.42 ± 4.023 7.63 ± 3.499 0.283


There is no significant difference in QRS score between two groups of patients who have DM and who don’t have DM (F=1.177, P= 0.283).

 

  HTN  

P-Value

NO YES
QRS Score 6.79 ± 3.635 6.96 ± 4.094 0.878

 

There is no significant difference in QRS score between two groups of patients who have HTN and who don’t have HTN (F=0.024, P= 0.878).

  DYSLIPIDEMIA

 

 

P-Value

NO YES
QRS Score 6.63 ± 3.691 7.90 ± 4.458 0.353


There is no significant difference in QRS score between two groups of patients who have DYSLIPIDEMIA and who don’t have (F=0.879, P= 0.353).

  F/H of IHD  

P-Value

NO YES
QRS Score 6.81 ± 3.614 7.29 ± 5.376 0.767

 

There is no significant difference in QRS score between two groups of patients who have F/H of IHD and who don’t have (F=0.089, P= 0.767).

ANOVA of QRS Score between Two Groups of Site of MI:

  SITE of MI  

P-Value

             Anterior Inferior
QRS Score 7.11  ± 4.182 6.61 ± 3.474 0.650


There is no significant difference in QRS score between Anterior and Inferior SITE of MI patients (F=0.209, P= 0.650).

 

Correlation between QRS Score and Vessel Score:

QRS score and Vessel score exhibits a significantly positive correlation (r=+0.972, p=0.000).Which concludes that QRS score and Vessel score tend to increase and decrease in the same direction.

 

d-1

Correlation between QRS Score and Friesinger Score:

QRS score and Friesinger score exhibits a significantly positive correlation (r=+0.989, p=0.000).Which concludes that QRS score and Vessel score tend to increase and decrease in the same direction.

d2

Regression Analysis of QRS score on Vessel score:

A simple regression analysis of QRS score has been performed on Vessel score which yields the following results:
Coefficients:

 

 

Model Summary:

R R square Adjusted R square
0.972 0.944 0.940


Which implies that the fitted regression model explains 94% of the total variation of Vessel score by QRS score.
The slope coefficient ẞ=0.193 implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.193 unit.

Regression Analysis of QRS score on Friesinger score:

A simple regression analysis of QRS score has been performed on Friesinger score which yields the following results:
Coefficients:

Model Summary:

R R square Adjusted R square
0.989 0.978 0.978


Which implies that the fitted regression model explains 97.8% of the total variation of Vessel score by QRS score.
The slope coefficient ẞ=0.777 implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.777 unit.

Conclusion:

In this cross sectional observational study, the association between QRS score with angiographic severity was studied through correlation and simple linear regression analysis. Also the mean difference among different groups of cardiovascular risk factors were studied through ANOVA (Analysis of Variance). Demographic characteristics were studied through descriptive analysis and also relationship of demographic variables with QRS score is studied through correlation analysis and ANOVA.
Descriptive analysis of the Quantitative variables reveals that the mean Age of 50 patients were 49.14
± 11.502; the average BMI of the patients were 26.48 ± 2.2283; the mean LVEF was
; the mean QRS score was 6.88 ± 3.842; the mean Vessel score was 1.78 ± 0.764 and the mean Friesinger score was 7.52 ± 3.019.

Bivariate correlation analysis and ANOVA was performed to study the relationship of demographic variables with QRS score. Correlation analysis reveals that there is no significant relationship between Age and QRS score, but there is a strong positive relationship between BMI and QRS score which is highly significant.
Analysis of Variance (ANOVA) suggests that there is a significant difference in QRS score between male and female group of patients.

Frequency distribution of Qualitative variables shows that 88% of the patients were male and 12% were female; 66% of the patients have the habit of smoking; 38% patients have DM; 52% patients have Dyslipidemia; only 14% patients have F/H of IHD; 54% patients have Anterior site of MI and 46% of the patients have Inferior site of MI.

Analysis of Variance (ANOVA) shows that there is significant difference in QRS score only between smoker and non-smoker group of patients, where there is no significant difference among other groups of cardiovascular risk factors. Also there is no significant difference between Anterior site of MI patients and Inferior site of MI patients.

To study the association between QRS score with angiographic severity we measure the correlation between QRS score and Vessel score, again the correlation between QRS score and Friesinger score. The correlation between QRS score and Vessel score (r= +0.972) indicates a significantly positive correlation between those scores, I.e, they tend to increase or decrease together. Similarly, the correlation between QRS score and Friesinger score (r=+0.989) indicates a significantly positive correlation between them, i.e, they tend to increase or decrease together.
To see the degree of dependence of angiographic severity on QRS score we fit two simple linear regression model- i) regression model of QRS score on Vessel score ii) regression model of QRS score on Friesinger score. The slope coefficient,ẞ=0.193 (p=0.00) of model(i) implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.193 unit. And the slope coefficient,ẞ=0.777 (p=0.00) of model(ii) implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.777 unit. Thus the angiographic severity is significantly dependent on QRS score.

Predicting the site of lesion in RCA by measuring the height of ST Segment elevation included a total of 50 patients of acute inferior MI.

The presents study aimed at predicting the site of lesion in RCA by measuring the height of ST Segment elevation included a total of 50 patients of acute inferior MI. With the help of 12-lead ECG, magnitudes of ST-seginent elevation in leads II, III and aVF were measured. The highest degree of stenosis along the RCA revealed by angiograms was accepted as the culprit lesion. Right coronary artery was divided into proximal (from its ostium to the origin of RV branch), mid (from the RV branch to the acute marginal branch) and distal (from the acute marginal branch onward) parts. The sum of ST segment elevation was then computed and compared among the three groups of patients divided on the basis of site of lesion in RCA. The findings obtained from data analyses are documented below.

 

Demographic characteristics:

Table I demonstrates the distribution of demographic characteristics among the three groups. The age distribution was almost homogeneous among the three groups (F =0.069, p =0.933). Males were predominant among the groups (x2 =0.630, p =0.730).

Table I. Demographic characteristics among the three groups

 

Demographic

characteristics

RCA Lesion

 

p-value
Proximal

(n = 26)

Mid

(n= 19)

Distal

(n = 05)

Age (year)# 54.6 ±5.2 54.6 ± 5.9 55.6 ± 4.4 0.933
Sex¶

Male

Female

23(88.5)

03(11.5)

17(89.5)

02(10.5)

05(100.0)

00(00)

0.730

# Data were analyzed using ANOVA statistics and are presented as mean ± SD;

¶ Data were analyzed using x2 Test.

Figures in the parentheses denote corresponding percentage.

Clinical presentation & examination:

The mean duration of chest pain was almost similar among the three groups (F = 3.046, p = 0.057). There was no significant difference in pulse rate among the groups (F =0.811, p =0.450). The mean systolic and diastolic blood pressures were significantly higher in the distal group than those in proximal and mid groups (F =7.983, p =0.001 and F =3.480, p=0.039 respectively). The proportion of patients with shortness of breath, sweating and nausea were significantly higher in the proximal group than those in other two groups; however vomiting was found identically distributed among the groups (x2 =8.864, p =0.012; x2 =13.543, p =0.001; x2 =10.499, p =0.005; x2 =3.338  p =0.188) (Table II).

Table II. Clinical presentation & examination among the three groups

 

Clinical presentations & Examination

RCA Lesion

 

p-value
Proximal

(n = 26)

Mid

(n = 19)

Distal

(n = 05)

Duration of chest pain#

(hours)

Pulse (beats/mm) #

Systolic BP (mm of hg) #

Diastolic BP(mm of Hg) #

 

Shortness of breath

Yes

No

Sweating

Yes

No

Nausea

Yes

No

Vomiting

Yes

No

7.0±1.16

72.8± 5.29

109 ± 11

73 ± 9

19(73.1)

7(26.9)

20 (76.9)

6(23.1)

16(61.5)

10(38.5)

14(53.8)

12(46.2)

7.74±0.73

75.4± 12.5

118± 17

82 ± 16

6(31.6)

13(68.9)

5(26.3)

14(73.7)

3(15.8)

16(84.2)

6(31.6)

13(68.4)

7.60±1.14

77.2±4.1

136± 18

82± 12

4(80.0)

1(20.0)

1(20.0)

4(80.0)

1(20.0)

4(80.0)

1(20.0)

4(80.0)

0.057

0.450

0.001

0.039

0.012

0.001

0.005

0.188

#Data were analyzed using ANOVA statistics and are presented as mean ± SD;

¶Data were analyzed using x2 Test.; figures in parentheses denote corresponding %.

7.3 Cardiovascular risk factors:

Table III shows that smoking habit, diabetes and dyslipidemia were significantly higher in the proximal group than those in mid and distal group (x =7.798, p =0.020; x2 =6.826, p =0.033 and x2 =10.499, p =0.005 respectively). Hypertension and family history of IHD were also higher in proximal group than those in other two groups, although the difference did not turn to significant (x2 =2.451, p =0.294 and x2 =0.685, p =0.710 respectively).

Table III. Cardiovascular risk factors among the three groups

 

Risk factors

RCA Lesion

 

p-value
Proximal

(n = 26)

Mid

(n = 19)

Distal

(n = 05)

Smoking habit

Yes

No

DM

Yes

No

HTN

Yes

No

Dyslipidaemia

Yes

No

Family H/O IHD

Yes

No

21(80.8)

5(19.2)

16(61.5)

10(38.5)

17(65.4)

9(34.6)

16(61.5)

10(38.5)

5(19.2)

21(80.8)

8(42.1)

11(57.9)

05(26.31)

14(73.68)

8(42.1)

11(57.9)

3(15.8)

16(84.2)

2(10.5)

17(89.5)

4(80.0)

1(20.0)

1(20.0)

3(80.0)

3(60.0)

2(40.0)

1(20.0)

4(80.0)

1(20.0)

4(80.0)

0.020

0.033

0.294

0.005

0.710

¶ Data were analyzed using x2 Test.

Figures in the parentheses denote corresponding percentage.

In-hospital complications:

Approximately three quarter (73.1%) of the patients in proximal group experienced hypotension,46.2% cardiogenic shock, 42.3% acute LVF and 46.2% arrhythmias. In mid group, about 15.8% of the patients had hypotension, 10.5% cardiogenic shock, 10.5% acute LVF and another 10.5% arrhythmias. 40% of patients in distal group had hypotension and 20% arrhythmias. All the in-hospital complications were observed to be significantly higher in the proximal group than those in other two groups (x2 =14.577, p =0.001; x2 =9.072, p =0.011; x2 =7.715, p =0.021; x2 =6.900, p=0.032) (Table IV).

Table IV. In-hospital complications among the three groups

 

Complications

RCA Lesion

 

p-value
Proximal

(n = 26)

Mid

(n = 19)

Distal

(n = 05)

Hypotension

Yes

No

Cardiogenic

shock

Yes

No

Acute LVF

Yes

No

Arrhythmia

Yes

No

19(73.1)

7(26.9)

12(46.2)

14(53.8)

11(42.3)

15(57.7)

12(46.2)

14(53.8)

3(15.8)

16(84.2)

2(10.5)

17(89.5)

2(10.5)

17(89.5)

2(10.5)

17(89.5)

1(20.0)

4(80.0)

0(0.0)

5(100.0)

0(0.0)

5(100.0)

1(20.0)

4(80.0)

0.001

0.011

0.021

0.032

¶ Data were analyzed using x2 Test.

Figures in the parentheses denote corresponding percentage.

 

 

 Echocardiograpnic findings:

Echocardiographic findings demonstrate that 20(76.9%) of 26 patients had regional wall motion abnormality (RWMA) in inferior wall in proximal group, 52.6% in mid group and 20% distal group (x =6.909, p=0.032). The mean percentage of left ventricular ejection fraction failure (LVEF) of proximal group had ( 45.67 ± 11.96), (49.31 ± 10.15) in mid and (57.28 ± 7.66) in distal group (F =2.493, p=0.094)

(Table V).

Table V.      Echocardiogram among the three groups

 Echocardiogram                                RCA Lesion
Proximal

(n = 26)

  Mid

(n = 19)

 Distal

(n = 05)

p-value
RWMA¶

Yes

No

LVEF(%)

20(76.9)

6(23.1)

45.67±11.96

10(52.6)

09(47.4)

49.31±10.15

1(20.0)

4(80.0)

57.28±7.66

0.032

0.094

¶Data were analyzed using x2 Test.

#Data were analyzed using ANOVA statistics and are presented as mean ± SD.

Note: In this study LV EF(%) were observed by echocardiography. As in this study among the acute inferior myocardial infarction 30% patient had RVI so measurement of RV EF might be more representative.

 

 

 Association of RVI with site of lesion in RCA:

More than half (53.8%) of the proximal lesions and 5.3% of the mid lesions in

RCA had RVI. None of the distal lesions had RVI.

Table VI. Association of RVI with site of lesion in RCA

RCA Lesion
RVI Proximal

(n = 26)

Mid

(n = 19)

Distal

(n = 05)

p-value
Present

Absent

14(53.8)

12(46.2)

1(5.3)

18(94.7)

0(0.0)

5(100.0)

0.001

¶ Data were analyzed using Chi-square (x2) Test.

Figures in the parentheses denote corresponding percentage.

ST segment elevation and site of lesion in RCA

The mean heights of ST-segment elevation in Lead II, Lead III and aVF and the

sum of ST-segment elevation showed a decreasing trend with progress of lesion

from proximal to distal site of RCA (F=78.660, p=0.000; F =87.123 p=0.000; F=34.438, p=0.000; F=157.747, p=0.000) (Table VII & Fig. 5).

Table VII. Association of ST segment elevation with site of lesion in RCA

ST segment elevatio #                              RCA

            (mm)                    

Proximal

(n = 48)

Mid

(n = 38)

Distal

(n = 14)

p-value
Lead II

Lead III

aVF

sum of ST segment

elevation

3.42 ± 0.42

4.83 ± 0.39

4.26 ± 0.85

12.52 ± 1.07

2.11 ± 0.44

3.60 ± 0.43

2.83 ± 0.45

8.54 ± 0.80

1.35 ± 0.45

2.60 ± 0.5

2.17 ± 0.24

6.15 ± 0.42

<0.001

<0.001

<0.001

<0.001

#Data were analyzed using ANOVA statistics and are presented as mean ± SD;

site-of-lesion-in-rca                  Showing relationship of height of ST elevation with site of lesion in RCA

Charts:

rca-lesion

pie-rca