I would like to calculate the prices in IQD. 1 USD is equal to 1458 IQD today.
There are 250, 500, 1,000, 5,000, 10,000, 25,000 and 50,000 denominations.
I need to sell my items based on USD. I have multicurrency plugin in my website. But it exchanges so precisely. It calculates smaller than cents.
For example, if I sell an item costs 1 dollar, the buyer need to pay 1458 IQD.
Is there anyway to calculate the prices what so called “near calculation”? Like, if an item costs 6820 it automatically shows 7000 at the checkout page. Or if it costs 6760, then shows 6750.
Thanks for your response.
unfortunately, there is no option in the plugin to do so.
I've had a bit of trouble explaining this so please bear with me. I'm also very new to using excel so if there's a simple fix, I apologize in advance!
I have two columns, one listing number of days starting from 0 and increasing consecutively. The other column has the number of orders delivered. The two correspond to each other. For example, I've typed out how it would look below. It would mean that there were 100 orders delivered in 1 day, 150 orders delivered in 2 days, 800 orders delivered in 3 days, etc.
Is there a way to get summary statistics (mean, median, mode, upper and lower quartiles) for the number of days it took for the average order to get delivered? The only way I can think of solving this is to manually punch in "1" 100 times, "2" 150 times, etc. into a new column and take median, mean, and upper & lower quartile from that, but that seems extremely inefficient. Would I use a pivot table for this? Thank you in advance!
I tried using the data analysis add-on and doing summary statistics that way, but it didn't work. It just gave me the mean, median, mode, and quartiles of each individual column. It would have given me 3 for median number of days for delivery and 300 for median number of orders.
Method 1
The mean is just
=SUMPRODUCT(A2:A6,B2:B6)/SUM(B2:B6)
Mode is the value with highest frequency
=INDEX(A2:A6,MATCH(MAX(B2:B6),B2:B6,0))
The quartiles and median (or any other quantile by varying the value of p) from first principles following this reference
=LET(p,0.25,
values,A2:A6,
freq,B2:B6,
N,SUM(freq),
h,(N+1)*p,
floorh,FLOOR(h,1),
ceilh,CEILING(h,1),
frac,h-floorh,
cusum,SCAN(0,SEQUENCE(ROWS(values)),LAMBDA(a,c,IF(c=1,0,a+INDEX(freq,c-1)))),
xlower,XLOOKUP(floorh-1,cusum,values,,-1),
xupper,XLOOKUP(ceilh-1,cusum,values,,-1),
xlower+(xupper-xlower)*frac)
Method 2
If you don't like doing it this way, you can always expand the data like this:
=AVERAGE(XLOOKUP(SEQUENCE(SUM(B2:B6),1,0),SCAN(0,SEQUENCE(ROWS(A2:A6)),LAMBDA(a,c,IF(c=1,0,INDEX(B2:B6,c-1)+a))),A2:A6,,-1))
=MODE(XLOOKUP(SEQUENCE(SUM(B2:B6),1,0),SCAN(0,SEQUENCE(ROWS(A2:A6)),LAMBDA(a,c,IF(c=1,0,INDEX(B2:B6,c-1)+a))),A2:A6,,-1))
=QUARTILE.EXC(XLOOKUP(SEQUENCE(SUM(B2:B6),1,0),SCAN(0,SEQUENCE(ROWS(A2:A6)),LAMBDA(a,c,IF(c=1,0,INDEX(B2:B6,c-1)+a))),A2:A6,,-1),1)
=MEDIAN(XLOOKUP(SEQUENCE(SUM(B2:B6),1,0),SCAN(0,SEQUENCE(ROWS(A2:A6)),LAMBDA(a,c,IF(c=1,0,INDEX(B2:B6,c-1)+a))),A2:A6,,-1))
and
=QUARTILE.EXC(XLOOKUP(SEQUENCE(SUM(B2:B6),1,0),SCAN(0,SEQUENCE(ROWS(A2:A6)),LAMBDA(a,c,IF(c=1,0,INDEX(B2:B6,c-1)+a))),A2:A6,,-1),3)
The Doobie Brothers garage band is planning a concert. Tickets are set at $20. Based on what other bands have done, they figure they should sell 350 tickets, but that could fluctuate. They figure the standard deviation of sales at 50 tickets. No shows are uniformly distributed between 1 and 10. Fixed costs are 5000.
How profitable is the concert likely to be?
So I am able to enter the excel formula for revenue 50*20 and subtract 5000 for FC, but I am having trouble deciphering how to account for the no show costs. I know that I have to use RANDBETWEEN(1,10) formula, but I am not sure if it gets multiplied or divided by something. Again, I am looking for what to do with the formula in the context of a profit equation.
If it helps, the mean for the number of tickets sold is 350 and stdev is 50, so I used that to get the number of attendees in a simulated sense...That is, NORM.INV(RAND(),350,50)
Of course, this problem may not be realistic in real life because promoters keep the money, but for the purposes of the problem...just assume that no promoters exist here.
The case:
Let us say a company is a monopolist in the market, and the supply of products is 100% met by demand every week. All (variable and fixed) costs are set. Based on estimations I have weekly units produced. In addition, the average price throughout the year has to hit approximately X. It is preferrable that the monthly results don't vary too much from break even. How can I quickly set the weekly price for the company’s product so that the company breaks even (Result = approx. 0), and at the same time keep the weekly price at the most stable price possible (as close to average as possible every week).
I have tried to use solver to minimize STD.DEV of prices (also tried skewness), with constraints that the average price is X and estimated result = 0, by changing the weekly prices throughoutthe year. However, this results in a few weeks of extreme price differences, which is the opposite of what I need.
Do somebody have a possible solution to the problem?
You could try the following:
minimize maxp - minp
maxp >= p(w) for all w
minp <= p(w) for all w
This will try to minimize the bandwidth (and additionally is completely linear)
Background
I wish to compare menu sales mix ratios for two periods.
A menu is defined as a collection of products. (i.e., a hamburger, a club sandwich, etc.)
A sales mix ratio is defined as a product's sales volume in units (i.e., 20 hamburgers) relative to the total number of menu units sold (i.e., 100 menu items were sold). In the hamburger example, the sales mix ratio for hamburgers is 20% (20 burgers / 100 menu items). This represents the share of total menu unit sales.
A period is defined as a time range used for comparative purposes (i.e., lunch versus dinner, Mondays versus Fridays, etc.).
I am not interested in overall changes in the volume (I don't care whether I sold 20 hamburgers in one period and 25 in another). I am only interested in changes in the distribution of the ratios (20% of my units sold were hamburgers in one period and 25% were hamburgers in another period).
Because the sales mix represents a share of the whole, the mean average for each period will be the same; the mean difference between the periods will always be 0%; and, the sum total for each set of data will always be 100%.
Objective:
Test whether the sales distribution (sales mix percentage of each menu item relative to other menu items) changed significantly from one period to another.
Null Hypothesis: the purchase patterns and preferences of customers in period A are the same as those for customers in period B.
Example of potential data input:
[Menu Item] [Period A] [Period B]
Hamburger 25% 28%
Cheeseburger 25% 20%
Salad 20% 25%
Club Sandwich 30% 27%
Question:
Do common methods exist to test whether the distribution of share-of-total is significantly different between two sets of data?
A paired T-Test would have worked if I was measuring a change in the number of actual units sold, but not (I believe) for a change in share of total units.
I've been searching online and a few text books for a while with no luck. I may be looking for the wrong terminology.
Any direction, be it search terms or (preferably) the actual names appropriate tests, are appreciated.
Thanks,
Andrew
EDIT: I am considering a Pearson Correlation test as a possible solution - forgetting that each row of data are independent menu items, the math shouldn't care. A perfect match (identical sales mix) would receive a coefficient of 1 and the greater the change the lower the coefficient would be. One potential issue is that unlike a regular correlation test, the changes may be amplified because any change to one number automatically impacts the others. Is this a viable solution? If so, is there a way to temper the amplification issue?
Consider using a Chi Squared Goodness-of-Fit test as a simple solution to this problem:
H0: the proportion of menu items for month B is the same as month A
Ha: at least one of the proportions of menu items for month B is
different to month A
There is a nice tutorial here.