Mathematician Matt Parker did a video on this subject where he points out an error by omission (ie. cherry-picking) and an error in the analysis. When those are corrected, we see the expected strong correlation between voting by candidate and voting by party for both candidates.
Biden votes show the same effect
If this is evidence Trump votes are being switched to Biden, we should see the opposite effect for Biden votes. Biden votes should show an upward trend.
We don't, we see exactly the same effect, just shifted upwards. The shift doesn't matter, it's the slope that does.
It's the slope that matters because what the graph is showing is that there is a strong correlation between candidate votes and party votes just as we'd expect, and just as Dr. Ayyadurai claims should be so, but inverted.
By showing only the Trump data, Dr. Ayyadurai is cherry-picking.
You cannot subtract percentages
They're inverted because of an elementary error in the analysis. You cannot subtract percentages unless you're sure the population sizes are the same. Dr. Ayyadurai subtracts the percentage of Wisconsin voters who voted for Trump by party vs those who vote for Trump by candidate. You can only subtract percentages if they're based on the same size population and this is not true for Wisconsin; percentages of party vs candidate voting there varies between about 45% and 80%.
Itachi0567 provides a good example for how this can go wrong. Consider a district with 1000 votes. 997 were GOP party votes. 2 were for Biden. 1 for Trump. That means voting by party is 100% Trump, but voting by candidate Trump gets 33%. Subtract them and you get -67%. By that logic, Trump got robbed in a district where he won 99.7% of the vote. The conclusion is flawed because it assumes the two populations are the same size.
If we're looking for a direct linear correlation between two variables we expect a line. The equation is Y = MX+B. Here X is
GOP Votes / Party Votes, Y is
Trump Votes / Candidate Votes, and M is how strongly they're correlated. Dr. Ayyadurai used
(Trump Votes / Candidate Votes) - (GOP Votes / Party Votes) for his Y-axis which is effectively Y-X. This has the effect of inverting the slope.
y = mx + b
y - x = mx + b - x
y - x = (m - 1)x + b
y - x = (m - 1)x + b is what Dr. Ayyadurai graphed.
m - 1 will cause a positive slope to go negative.
What should have been plotted is
Trump Votes / Candidate Votes vs
GOP Votes / Party Votes per district. When we do that, we see the expected strong correlation for both parties.
Let's acknowledge the problem with these voter fraud analyses: they are fishing expeditions and subject to confirmation bias.
These fishing expeditions are fueled by Trump's unsupported claims of voter fraud. Trump claims there is evidence, but he has repeatedly shown none. And for most people that's the end.
Some people go hunting for evidence to support the conclusion ex post facto. These people may be well meaning, but are liable to be less critical of their analysis when it yields a positive result. This is yet another example.
An honest analysis of the integrity of US election would not presume an outcome, would not be hunting to prove a claim ex post facto, and would critically review their analysis. Such analyses have been conducted again and again and find no evidence for widespread voter fraud.